NaN output cause?

[xEmz]

Hi, I got MAMA to work. But now I have an issue I can't seem to figure out. I keep getting NaN output because something goes wrong with calculating the omega. When I only run European data I do get output, but when I analyze it with African and Latin data, it seems to drop SNPs.

What could be the issue?

Verbose log file:

2025-02-18 15:29:43,197
<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
<>
<> MAMA: Multi-Ancestry Meta-Analysis
<> Version: 1.0.0
<> (C) 2020 Social Science Genetic Association Consortium (SSGAC)
<> MIT License
<>
<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
<> Software-related correspondence: jjala.ssgac@gmail.com
<> All other correspondence: paturley@broadinstitute.org
<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

2025-02-18 15:29:43,197 See full log at: /hpc/dhl_ec/esmulders/mama/cIMT_FEMALES_MAMA.log

2025-02-18 15:29:43,197
Program executed via:
./mama.py \
--sumstats /hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_EUR/females_eur/META/input/CHARGE_cIMT_FEMALES_EUR.mama.b37.gwaslab.qc.txt.gz,EUR,FEMALES_cIMT /hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_AFR/females_afr/META/input/CHARGE_cIMT_FEMALES_AFR.mama.b37.gwaslab.qc.txt.gz,AFR,FEMALES_cIMT \
--ld-scores /hpc/dhl_ec/esmulders/references/MAMA/1kgp3v5.hg19.split_norm_af.AFR_AMR_EUR.FEMALES_maf0_01_geno0_10.ldscore.ldscores.txt \
--out ./cIMT_FEMALES_MAMA \
--verbose \
--use-standardized-units \
--allow-palindromic-snps \
--input-sep \t

2025-02-18 15:29:43,197 Printing Pandas' version summary:
2025-02-18 15:29:43,340
INSTALLED VERSIONS

commit : 478d340667831908b5b4bf09a2787a11a14560c9
python : 3.9.21.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.0-553.30.1.el8_10.x86_64
Version : #1 SMP Tue Nov 26 18:56:25 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.0.0
numpy : 1.23.0
pytz : 2025.1
dateutil : 2.9.0.post0
setuptools : 75.8.0
pip : 25.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.11.1
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2025.1
qtpy : None
pyqt5 : None

2025-02-18 15:29:43,341
Program was called with the following arguments:
{'use_standardized_units': True, 'allow_palindromic_snps': True, 'ld_scores': ['/hpc/dhl_ec/esmulders/references/MAMA/1kgp3v5.hg19.split_norm_af.AFR_AMR_EUR.FEMALES_maf0_01_geno0_10.ldscore.ldscores.txt'], 'sumstats': [('/hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_EUR/females_eur/META/input/CHARGE_cIMT_FEMALES_EUR.mama.b37.gwaslab.qc.txt.gz', 'EUR', 'FEMALES_cIMT'), ('/hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_AFR/females_afr/META/input/CHARGE_cIMT_FEMALES_AFR.mama.b37.gwaslab.qc.txt.gz', 'AFR', 'FEMALES_cIMT')], 'out': './cIMT_FEMALES_MAMA', 'input_sep': '\t', 'verbose': True}
2025-02-18 15:29:43,401
Regex map = {'SNP': '.SNP.|.RS.', 'BP': '.BP.|.POS.', 'CHR': '.CHR.', 'BETA': '.BETA.', 'FREQ': '.FREQ.|.FRQ.|.*AF', 'SE': '.SE.', 'A1': '.A1.|.MAJOR.|.EFFECT.ALL.|REF.', 'A2': '.A2.|.MINOR.|.OTHER.ALL.|ALT.', 'P': 'P|P.VAL.', 'INFO': 'INFO', 'N': 'N'}
2025-02-18 15:29:43,406
Filter map = {'NO NAN': (<function at 0x7f18d4eceaf0>, "Filters out SNPs with any NaN values in required columns {'FREQ', 'BETA', 'CHR', 'P', 'SE', 'BP', 'A1', 'SNP', 'A2'}"), 'FREQ BOUNDS': (<function create_freq_filter.. at 0x7f18cd9d03a0>, 'Filters out SNPs with FREQ values outside of [0.01, 0.99]'), 'SE BOUNDS': (<function at 0x7f18cd9d0430>, 'Filters out SNPs with non-positive SE values'), 'CHR VALUES': (<function create_chr_filter.. at 0x7f18cd9d04c0>, "Filters out SNPs with listed chromosomes not in ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', 'X', 'Y']"), 'DUPLICATE ALLELE SNPS': (<function at 0x7f18cd9d0550>, 'Filters out SNPs with major allele = minor allele'), 'INVALID ALLELES': (<function at 0x7f18cd9d0670>, "Filters out SNPs with alleles not in {'T', 'G', 'C', 'A'}"), 'NEGATIVE GWAS P': (<function at 0x7f18cd9d0700>, 'Filters out SNPs with negative GWAS P values')}

2025-02-18 15:29:43,406 Regression coeffient option (LD) = all_unconstrained
2025-02-18 15:29:43,406 Regression coeffient option (LD Scale) = None
2025-02-18 15:29:43,406 Regression coeffient option (Intercept) = all_unconstrained
2025-02-18 15:29:43,406 Regression coeffient option (SE^2) = offdiag_zero
2025-02-18 15:29:43,406

Reading in and running QC on LD Scores

2025-02-18 15:29:43,406
List of files: ['/hpc/dhl_ec/esmulders/references/MAMA/1kgp3v5.hg19.split_norm_af.AFR_AMR_EUR.FEMALES_maf0_01_geno0_10.ldscore.ldscores.txt']
2025-02-18 15:29:43,406 Reading in LD Scores file: /hpc/dhl_ec/esmulders/references/MAMA/1kgp3v5.hg19.split_norm_af.AFR_AMR_EUR.FEMALES_maf0_01_geno0_10.ldscore.ldscores.txt
2025-02-18 15:31:45,171

Reading in summary statistics.
2025-02-18 15:31:45,172

2025-02-18 15:31:45,172 Reading in ('EUR', 'FEMALES_cIMT') sumstats file: /hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_EUR/females_eur/META/input/CHARGE_cIMT_FEMALES_EUR.mama.b37.gwaslab.qc.txt.gz
2025-02-18 15:32:48,331
Running QC on ('EUR', 'FEMALES_cIMT') summary statistics
2025-02-18 15:32:48,332 Column mapping = {'BP': 'BP', 'Chr': 'CHR', 'rsID': 'SNP', 'A1': 'A1', 'A2': 'A2', 'EAF': 'FREQ', 'BETA': 'BETA', 'P': 'P', 'SE': 'SE', 'N': 'N'}

2025-02-18 15:32:48,333 First set of rows from initial reading of summary stats:
BP Chr rsID A1 A2 EAF BETA P SE N
0 10177 1 1:10177:A:AC AC A 0.401 -0.0005 0.81272 0.0022 11540
1 10352 1 1:10352:T:TA TA T 0.403 0.0004 0.84544 0.0023 11113
2 14599 1 1:14599:T:A A T 0.189 -0.0023 0.41643 0.0029 10585
3 14930 1 1:14930:A:G G A 0.539 0.0034 0.12661 0.0022 10935
4 15903 1 1:15903:G:GC GC G 0.412 -0.0035 0.10523 0.0022 11659
2025-02-18 15:32:48,345
Initial number of SNPs / rows = 8986320

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2025-02-18 15:33:06,577 Filtered out 0 SNPs with "NEGATIVE GWAS P" (Filters out SNPs with negative GWAS P values)
2025-02-18 15:33:06,577 RS IDs = []
2025-02-18 15:33:06,580

Filtered out 564607 SNPs in total (as the union of drops, this may be less than the total of all the per-filter drops)
2025-02-18 15:33:06,580 Additionally dropped 0 duplicate SNPs
2025-02-18 15:33:06,580 RS IDs = []
2025-02-18 15:33:06,592

2025-02-18 15:33:06,592 Reading in ('AFR', 'FEMALES_cIMT') sumstats file: /hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_AFR/females_afr/META/input/CHARGE_cIMT_FEMALES_AFR.mama.b37.gwaslab.qc.txt.gz
2025-02-18 15:34:22,988
Running QC on ('AFR', 'FEMALES_cIMT') summary statistics
2025-02-18 15:34:22,989 Column mapping = {'BP': 'BP', 'Chr': 'CHR', 'rsID': 'SNP', 'A1': 'A1', 'A2': 'A2', 'EAF': 'FREQ', 'BETA': 'BETA', 'P': 'P', 'SE': 'SE', 'N': 'N'}

2025-02-18 15:34:22,990 First set of rows from initial reading of summary stats:
BP Chr rsID A1 A2 EAF BETA P SE N
0 662622 1 1:662622:G:A A G 0.202 0.0882 0.057074 0.0463 1148
1 693731 1 1:693731:A:G G A 0.196 0.0906 0.052977 0.0468 1165
2 693823 1 1:693823:G:C C G 0.211 0.0694 0.131840 0.0461 1174
3 703942 1 1:703942:G:C C G 0.189 0.0234 0.615080 0.0466 1292
4 705452 1 1:705452:T:A A T 0.193 0.0238 0.607910 0.0463 1299
2025-02-18 15:34:22,998
Initial number of SNPs / rows = 10011608

2025-02-18 15:34:45,599 Filtered out 0 SNPs with "NO NAN" (Filters out SNPs with any NaN values in required columns {'FREQ', 'BETA', 'CHR', 'P', 'SE', 'BP', 'A1', 'SNP', 'A2'})
2025-02-18 15:34:45,599 RS IDs = []
2025-02-18 15:34:45,599 Filtered out 0 SNPs with "FREQ BOUNDS" (Filters out SNPs with FREQ values outside of [0.01, 0.99])
2025-02-18 15:34:45,599 RS IDs = []
2025-02-18 15:34:45,599 Filtered out 0 SNPs with "SE BOUNDS" (Filters out SNPs with non-positive SE values)
2025-02-18 15:34:45,599 RS IDs = []
2025-02-18 15:34:45,599 Filtered out 0 SNPs with "CHR VALUES" (Filters out SNPs with listed chromosomes not in ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', 'X', 'Y'])
2025-02-18 15:34:45,599 RS IDs = []
2025-02-18 15:34:45,599 Filtered out 0 SNPs with "DUPLICATE ALLELE SNPS" (Filters out SNPs with major allele = minor allele)
2025-02-18 15:34:45,599 RS IDs = []
2025-02-18 15:34:45,599 Filtered out 510337 SNPs with "INVALID ALLELES" (Filters out SNPs with alleles not in {'T', 'G', 'C', 'A'})
2025-02-18 15:34:45,599 RS IDs = ['1:723738:act:a', '1:746037:tac:t', '1:752509:ca:c', '1:764261:tca:t', '1:769138:cat:c', '1:774546:tgaga:t', '1:779797:g:gctcc', '1:780347:tttaa:t', '1:781057:tc:t', '1:789513:ga:g', '1:819121:c:cat', '1:821325:acagt:a', '1:821862:cacagcagctgtgctgtgtt:c', '1:827712:t:ta', '1:832297:ctg:c', '1:833172:t:tcgaa', '1:834637:ac:a', '1:837553:ggtgt:g', '1:838329:g:gc', '1:846600:ct:c', '1:848654:tg:t', '1:848828:ga:g', '1:856513:tc:t', '1:862822:g:gc', '1:875159:agccagtggacgccgacct:a', '1:880639:tc:t', '1:884549:cagag:c', '1:886831:g:gggtca', '1:895037:a:ag', '1:900717:cttat:c', '1:904868:atg:a', '1:905043:cat:c', '1:907170:ag:a', '1:919525:tagtc:t', '1:922305:g:gc', '1:925551:at:a', '1:928849:cag:c', '1:938709:ggggtggatcctgggctgca:g', '1:939491:tg:t', '1:940809:c:ca', '1:945135:g:ga', '1:948846:t:ta', '1:950113:gaagt:g', '1:962036:a:ag', '1:963466:acact:a', '1:975992:gacgtgggt:g', '1:978147:a:ag', '1:978603:cct:c', '1:994395:ag:a', '1:995305:ct:c', '...']
2025-02-18 15:34:45,599 Filtered out 0 SNPs with "NEGATIVE GWAS P" (Filters out SNPs with negative GWAS P values)
2025-02-18 15:34:45,599 RS IDs = []
2025-02-18 15:34:45,603

Filtered out 510337 SNPs in total (as the union of drops, this may be less than the total of all the per-filter drops)
2025-02-18 15:34:45,604 Additionally dropped 0 duplicate SNPs
2025-02-18 15:34:45,604 RS IDs = []
2025-02-18 15:34:50,793

Number of SNPS in initial intersection of all sources: 5224321
2025-02-18 15:35:10,041
Standardizing reference alleles in summary statistics.
2025-02-18 15:35:19,861 Standardized to population: ('EUR', 'FEMALES_cIMT')
2025-02-18 15:35:19,863 Dropped 0 SNPs during reference allele standardization.
2025-02-18 15:35:19,864 RS IDs of drops during standardization: []
2025-02-18 15:35:24,990

2025-02-18 15:35:25,013 Harmonized EUR FEMALES_cIMT mean chi squared: 1.0277880531787356
2025-02-18 15:35:25,033 Harmonized AFR FEMALES_cIMT mean chi squared: 1.0146716506139801
2025-02-18 15:35:25,033

2025-02-18 15:35:25,387 Converting ('EUR', 'FEMALES_cIMT') to standardized units
2025-02-18 15:35:25,459 Converting ('AFR', 'FEMALES_cIMT') to standardized units
2025-02-18 15:35:25,650

Running LD Score regression.
2025-02-18 15:35:25,650 Options = {'ld_fixed_opt': 'all_unconstrained', 'se_fixed_opt': 'offdiag_zero', 'int_fixed_opt': 'all_unconstrained', 'ld_scale_factor': None}
2025-02-18 15:35:26,750 Received Numpy error: divide by zero (1)
2025-02-18 15:35:27,027 Regression coefficients (LD):
[[-2.11877785e-07 -4.30949835e-05]
[-4.30949835e-05 -1.58572224e-05]]
2025-02-18 15:35:27,027 Regression coefficients (Intercept):
[[ 2.07948130e-06 -3.34591361e-06]
[-3.34591361e-06 3.59172410e-04]]
2025-02-18 15:35:27,028 Regression coefficients (SE^2):
[[0.4876061 0. ]
[0. 0.33165499]]
2025-02-18 15:35:27,028

Creating omega and sigma matrices.
2025-02-18 15:36:29,023 Average Omega (including dropped slices) =
[[-9.60234438e-07 -1.11267192e-04]
[-1.11267192e-04 -6.36156502e-05]]
2025-02-18 15:36:29,105 Average Sigma (including dropped slices) =
[[ 3.13543520e-06 -3.34591361e-06]
[-3.34591361e-06 5.03155489e-04]]
2025-02-18 15:36:29,108
Adjusted 0 SNPs to make omega positive semi-definite.
2025-02-18 15:36:29,109 RS IDs = []
2025-02-18 15:36:29,112
Dropped 5224321 SNPs due to non-positive-semi-definiteness of omega.
2025-02-18 15:36:29,321 RS IDs = ['10:10000018:a:g', '10:100000625:a:g', '10:100000645:a:c', '10:100003242:t:g', '10:100003785:t:c', '10:100004360:g:a', '10:100004906:c:a', '10:100004996:g:a', '10:10000514:t:c', '10:100005282:c:t', '10:100007362:g:c', '10:100008436:g:a', '10:100010186:a:g', '10:10001085:g:a', '10:100011114:t:a', '10:100011970:g:a', '10:10001208:t:c', '10:100012739:a:g', '10:100012890:a:g', '10:100013244:a:c', '10:100013438:c:t', '10:100013563:c:t', '10:100013815:c:t', '10:100013977:a:t', '10:100015563:g:c', '10:100015603:g:t', '10:100016313:a:t', '10:100016339:c:t', '10:100017453:t:g', '10:100018238:c:t', '10:100018844:g:a', '10:100019039:g:t', '10:100020572:t:g', '10:100020880:c:t', '10:10002142:g:a', '10:100021533:a:g', '10:100021672:c:t', '10:10002186:g:a', '10:100021983:g:a', '10:100023208:c:t', '10:100023359:t:c', '10:100023614:a:g', '10:100023857:c:g', '10:100023957:t:g', '10:100024195:g:t', '10:100024848:g:a', '10:100025095:t:c', '10:100025816:g:a', '10:100025924:a:g', '10:100026127:t:a', '...']
2025-02-18 15:36:29,324 Dropped 0 SNPs due to non-positive-definiteness of sigma.
2025-02-18 15:36:29,325 RS IDs = []
2025-02-18 15:36:29,328 Dropped 5224321 total SNPs due to non-positive-(semi)-definiteness of omega / sigma.
2025-02-18 15:36:29,460 RS IDs = ['10:10000018:a:g', '10:100000625:a:g', '10:100000645:a:c', '10:100003242:t:g', '10:100003785:t:c', '10:100004360:g:a', '10:100004906:c:a', '10:100004996:g:a', '10:10000514:t:c', '10:100005282:c:t', '10:100007362:g:c', '10:100008436:g:a', '10:100010186:a:g', '10:10001085:g:a', '10:100011114:t:a', '10:100011970:g:a', '10:10001208:t:c', '10:100012739:a:g', '10:100012890:a:g', '10:100013244:a:c', '10:100013438:c:t', '10:100013563:c:t', '10:100013815:c:t', '10:100013977:a:t', '10:100015563:g:c', '10:100015603:g:t', '10:100016313:a:t', '10:100016339:c:t', '10:100017453:t:g', '10:100018238:c:t', '10:100018844:g:a', '10:100019039:g:t', '10:100020572:t:g', '10:100020880:c:t', '10:10002142:g:a', '10:100021533:a:g', '10:100021672:c:t', '10:10002186:g:a', '10:100021983:g:a', '10:100023208:c:t', '10:100023359:t:c', '10:100023614:a:g', '10:100023857:c:g', '10:100023957:t:g', '10:100024195:g:t', '10:100024848:g:a', '10:100025095:t:c', '10:100025816:g:a', '10:100025924:a:g', '10:100026127:t:a', '...']
2025-02-18 15:36:29,460

Running main MAMA method.
2025-02-18 15:36:37,462
Preparing results for output.

2025-02-18 15:36:37,462 Population 0: ('EUR', 'FEMALES_cIMT')
2025-02-18 15:36:47,576 Received Numpy error: invalid value (8)
2025-02-18 15:36:47,576 Mean Chi^2 for ('EUR', 'FEMALES_cIMT') = nan
2025-02-18 15:36:47,576 Converting ('EUR', 'FEMALES_cIMT') from standardized units
2025-02-18 15:36:47,577 Population 1: ('AFR', 'FEMALES_cIMT')
2025-02-18 15:36:57,791 Received Numpy error: invalid value (8)
2025-02-18 15:36:57,791 Mean Chi^2 for ('AFR', 'FEMALES_cIMT') = nan
2025-02-18 15:36:57,791 Converting ('AFR', 'FEMALES_cIMT') from standardized units
2025-02-18 15:36:58,075
Final SNP count = 0
2025-02-18 15:36:58,751 Writing results to disk.
2025-02-18 15:36:58,751 ./cIMT_FEMALES_MAMA_EUR_FEMALES_cIMT.res
2025-02-18 15:36:58,759 ./cIMT_FEMALES_MAMA_AFR_FEMALES_cIMT.res
2025-02-18 15:36:58,761
Execution complete.

[paturley]

Hi! It looks like you have too low power for MAMA. The mean chi2 statistics for the two data sets you used in this log file are 1.01 and 1.02, and I really like to see these as high as 1.1 (and honestly even higher) before I expect MAMA to work robustly. The problem with low power is that the implied variance of effect sizes is negative, which causes the MAMA math to break down. I suspect that it works when you have just EUR sum stats because there are a few SNPs that are estimated to have negative LD scores, so when the negative LD scores are multiplied by a negative variance, the output is positive, so MAMA thinks those SNPs are fine. Sorry I can't be more help. Best, Patrick

…

On Tue, Feb 18, 2025 at 9:42 AM Emma Smulders ***@***.***> wrote: Hi, I got MAMA to work. But now I have an issue I can't seem to figure out. I keep getting NaN output because something goes wrong with calculating the omega. When I only run European data I do get output, but when I analyze it with African and Latin data, it seems to drop SNPs. What could be the issue? Verbose log file: 2025-02-18 15:29:43,197 <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> <> <> MAMA: Multi-Ancestry Meta-Analysis <> Version: 1.0.0 <> (C) 2020 Social Science Genetic Association Consortium (SSGAC) <> MIT License <> <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> <> Software-related correspondence: ***@***.*** <> All other correspondence: ***@***.*** <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> 2025-02-18 15:29:43,197 See full log at: /hpc/dhl_ec/esmulders/mama/cIMT_FEMALES_MAMA.log 2025-02-18 15:29:43,197 Program executed via: ./mama.py \ --sumstats /hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_EUR/females_eur/META/input/CHARGE_cIMT_FEMALES_EUR.mama.b37.gwaslab.qc.txt.gz,EUR,FEMALES_cIMT /hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_AFR/females_afr/META/input/CHARGE_cIMT_FEMALES_AFR.mama.b37.gwaslab.qc.txt.gz,AFR,FEMALES_cIMT \ --ld-scores /hpc/dhl_ec/esmulders/references/MAMA/1kgp3v5.hg19.split_norm_af.AFR_AMR_EUR.FEMALES_maf0_01_geno0_10.ldscore.ldscores.txt \ --out ./cIMT_FEMALES_MAMA \ --verbose \ --use-standardized-units \ --allow-palindromic-snps \ --input-sep \t 2025-02-18 15:29:43,197 Printing Pandas' version summary: 2025-02-18 15:29:43,340 INSTALLED VERSIONS commit : 478d340667831908b5b4bf09a2787a11a14560c9 python : 3.9.21.final.0 python-bits : 64 OS : Linux OS-release : 4.18.0-553.30.1.el8_10.x86_64 Version : #1 <#1> SMP Tue Nov 26 18:56:25 UTC 2024 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 2.0.0 numpy : 1.23.0 pytz : 2025.1 dateutil : 2.9.0.post0 setuptools : 75.8.0 pip : 25.0.1 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None brotli : None fastparquet : None fsspec : None gcsfs : None matplotlib : None numba : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : 1.11.1 snappy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None zstandard : None tzdata : 2025.1 qtpy : None pyqt5 : None 2025-02-18 15:29:43,341 Program was called with the following arguments: {'use_standardized_units': True, 'allow_palindromic_snps': True, 'ld_scores': ['/hpc/dhl_ec/esmulders/references/MAMA/1kgp3v5.hg19.split_norm_af.AFR_AMR_EUR.FEMALES_maf0_01_geno0_10.ldscore.ldscores.txt'], 'sumstats': [('/hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_EUR/females_eur/META/input/CHARGE_cIMT_FEMALES_EUR.mama.b37.gwaslab.qc.txt.gz', 'EUR', 'FEMALES_cIMT'), ('/hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_AFR/females_afr/META/input/CHARGE_cIMT_FEMALES_AFR.mama.b37.gwaslab.qc.txt.gz', 'AFR', 'FEMALES_cIMT')], 'out': './cIMT_FEMALES_MAMA', 'input_sep': '\t', 'verbose': True} 2025-02-18 15:29:43,401 Regex map = {'SNP': '.*SNP.*|.*RS.*', 'BP': '.*BP.*|.*POS.*', 'CHR': '. *CHR.*', 'BETA': '.*BETA.*', 'FREQ': '.*FREQ.*|.*FRQ.*|.*AF', 'SE': '. *SE.*', 'A1': '.*A1.*|.*MAJOR.*|.*EFFECT.ALL.|REF.*', 'A2': '.*A2.*|. *MINOR.*|.*OTHER.ALL.|ALT.*', 'P': 'P|P.*VAL.*', 'INFO': 'INFO', 'N': 'N'} 2025-02-18 15:29:43,406 Filter map = {'NO NAN': (<function at 0x7f18d4eceaf0>, "Filters out SNPs with any NaN values in required columns {'FREQ', 'BETA', 'CHR', 'P', 'SE', 'BP', 'A1', 'SNP', 'A2'}"), 'FREQ BOUNDS': (<function create_freq_filter.. at 0x7f18cd9d03a0>, 'Filters out SNPs with FREQ values outside of [0.01, 0.99]'), 'SE BOUNDS': (<function at 0x7f18cd9d0430>, 'Filters out SNPs with non-positive SE values'), 'CHR VALUES': (<function create_chr_filter.. at 0x7f18cd9d04c0>, "Filters out SNPs with listed chromosomes not in ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', 'X', 'Y']"), 'DUPLICATE ALLELE SNPS': (<function at 0x7f18cd9d0550>, 'Filters out SNPs with major allele = minor allele'), 'INVALID ALLELES': (<function at 0x7f18cd9d0670>, "Filters out SNPs with alleles not in {'T', 'G', 'C', 'A'}"), 'NEGATIVE GWAS P': (<function at 0x7f18cd9d0700>, 'Filters out SNPs with negative GWAS P values')} 2025-02-18 15:29:43,406 Regression coeffient option (LD) = all_unconstrained 2025-02-18 15:29:43,406 Regression coeffient option (LD Scale) = None 2025-02-18 15:29:43,406 Regression coeffient option (Intercept) = all_unconstrained 2025-02-18 15:29:43,406 Regression coeffient option (SE^2) = offdiag_zero 2025-02-18 15:29:43,406 Reading in and running QC on LD Scores 2025-02-18 15:29:43,406 List of files: ['/hpc/dhl_ec/esmulders/references/MAMA/1kgp3v5.hg19.split_norm_af.AFR_AMR_EUR.FEMALES_maf0_01_geno0_10.ldscore.ldscores.txt'] 2025-02-18 15:29:43,406 Reading in LD Scores file: /hpc/dhl_ec/esmulders/references/MAMA/1kgp3v5.hg19.split_norm_af.AFR_AMR_EUR.FEMALES_maf0_01_geno0_10.ldscore.ldscores.txt 2025-02-18 15:31:45,171 Reading in summary statistics. 2025-02-18 15:31:45,172 2025-02-18 15:31:45,172 Reading in ('EUR', 'FEMALES_cIMT') sumstats file: /hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_EUR/females_eur/META/input/CHARGE_cIMT_FEMALES_EUR.mama.b37.gwaslab.qc.txt.gz 2025-02-18 15:32:48,331 Running QC on ('EUR', 'FEMALES_cIMT') summary statistics 2025-02-18 15:32:48,332 Column mapping = {'BP': 'BP', 'Chr': 'CHR', 'rsID': 'SNP', 'A1': 'A1', 'A2': 'A2', 'EAF': 'FREQ', 'BETA': 'BETA', 'P': 'P', 'SE': 'SE', 'N': 'N'} 2025-02-18 15:32:48,333 First set of rows from initial reading of summary stats: BP Chr rsID A1 A2 EAF BETA P SE N 0 10177 1 1:10177:A:AC AC A 0.401 -0.0005 0.81272 0.0022 11540 1 10352 1 1:10352:T:TA TA T 0.403 0.0004 0.84544 0.0023 11113 2 14599 1 1:14599:T:A A T 0.189 -0.0023 0.41643 0.0029 10585 3 14930 1 1:14930:A:G G A 0.539 0.0034 0.12661 0.0022 10935 4 15903 1 1:15903:G:GC GC G 0.412 -0.0035 0.10523 0.0022 11659 2025-02-18 15:32:48,345 Initial number of SNPs / rows = 8986320 ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� '1:15903:g:gc', '1:63735:ccta:c', '1:129010:aatg:a', '1:249275:g:gt', '1:251627:ac:a', '1:255923:g:gtc', '1:540540:gc:g', '1:612688:tctc:t', '1:668394:ag:a', '1:701779:gaata:g', '1:713131:a:at', '1:732134:t:ta', '1:733581:ca:c', '1:745642:ac:a', '1:746211:a:ag', '1:747753:tgc:t', '1:752307:at:a', '1:760811:ctctt:c', '1:761957:a:at', '1:765155:ttgac:t', '1:769138:cat:c', '1:787069:cag:c', '1:789513:ga:g', '1:797126:gtaat:g', '1:821325:acagt:a', '1:832297:ctg:c', '1:832960:at:a', '1:833172:t:tcgaa', '1:834637:ac:a', '1:837553:ggtgt:g', '1:838329:g:gc', '1:842057:a:aaactcagctgcctctccccttc', '1:842387:a:acc', '1:846600:ct:c', '1:848654:tg:t', '1:849863:g:gactgcccagctc', '1:850425:g:ggtcc', '1:852011:agagccggccct:a', '1:858691:tg:t', '1:868928:a:ag', '1:871042:c:ca', '1:874950:t:tccctggaggacc', '1:875159:agccagtggacgccgacct:a', '1:880639:tc:t', '1:886179:ca:c', '1:893461:tc:t', '1:895755:a:ag', '1:900717:cttat:c', '...'] 2025-02-18 15:33:06,577 Filtered out 0 SNPs with "NEGATIVE GWAS P" (Filters out SNPs with negative GWAS P values) 2025-02-18 15:33:06,577 RS IDs = [] 2025-02-18 15:33:06,580 Filtered out 564607 SNPs in total (as the union of drops, this may be less than the total of all the per-filter drops) 2025-02-18 15:33:06,580 Additionally dropped 0 duplicate SNPs 2025-02-18 15:33:06,580 RS IDs = [] 2025-02-18 15:33:06,592 2025-02-18 15:33:06,592 Reading in ('AFR', 'FEMALES_cIMT') sumstats file: /hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_AFR/females_afr/META/input/CHARGE_cIMT_FEMALES_AFR.mama.b37.gwaslab.qc.txt.gz 2025-02-18 15:34:22,988 Running QC on ('AFR', 'FEMALES_cIMT') summary statistics 2025-02-18 15:34:22,989 Column mapping = {'BP': 'BP', 'Chr': 'CHR', 'rsID': 'SNP', 'A1': 'A1', 'A2': 'A2', 'EAF': 'FREQ', 'BETA': 'BETA', 'P': 'P', 'SE': 'SE', 'N': 'N'} 2025-02-18 15:34:22,990 First set of rows from initial reading of summary stats: BP Chr rsID A1 A2 EAF BETA P SE N 0 662622 1 1:662622:G:A A G 0.202 0.0882 0.057074 0.0463 1148 1 693731 1 1:693731:A:G G A 0.196 0.0906 0.052977 0.0468 1165 2 693823 1 1:693823:G:C C G 0.211 0.0694 0.131840 0.0461 1174 3 703942 1 1:703942:G:C C G 0.189 0.0234 0.615080 0.0466 1292 4 705452 1 1:705452:T:A A T 0.193 0.0238 0.607910 0.0463 1299 2025-02-18 15:34:22,998 Initial number of SNPs / rows = 10011608 2025-02-18 15:34:45,599 Filtered out 0 SNPs with "NO NAN" (Filters out SNPs with any NaN values in required columns {'FREQ', 'BETA', 'CHR', 'P', 'SE', 'BP', 'A1', 'SNP', 'A2'}) 2025-02-18 15:34:45,599 RS IDs = [] 2025-02-18 15:34:45,599 Filtered out 0 SNPs with "FREQ BOUNDS" (Filters out SNPs with FREQ values outside of [0.01, 0.99]) 2025-02-18 15:34:45,599 RS IDs = [] 2025-02-18 15:34:45,599 Filtered out 0 SNPs with "SE BOUNDS" (Filters out SNPs with non-positive SE values) 2025-02-18 15:34:45,599 RS IDs = [] 2025-02-18 15:34:45,599 Filtered out 0 SNPs with "CHR VALUES" (Filters out SNPs with listed chromosomes not in ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', 'X', 'Y']) 2025-02-18 15:34:45,599 RS IDs = [] 2025-02-18 15:34:45,599 Filtered out 0 SNPs with "DUPLICATE ALLELE SNPS" (Filters out SNPs with major allele = minor allele) 2025-02-18 15:34:45,599 RS IDs = [] 2025-02-18 15:34:45,599 Filtered out 510337 SNPs with "INVALID ALLELES" (Filters out SNPs with alleles not in {'T', 'G', 'C', 'A'}) 2025-02-18 15:34:45,599 RS IDs = ['1:723738:act:a', '1:746037:tac:t', '1:752509:ca:c', '1:764261:tca:t', '1:769138:cat:c', '1:774546:tgaga:t', '1:779797:g:gctcc', '1:780347:tttaa:t', '1:781057:tc:t', '1:789513:ga:g', '1:819121:c:cat', '1:821325:acagt:a', '1:821862:cacagcagctgtgctgtgtt:c', '1:827712:t:ta', '1:832297:ctg:c', '1:833172:t:tcgaa', '1:834637:ac:a', '1:837553:ggtgt:g', '1:838329:g:gc', '1:846600:ct:c', '1:848654:tg:t', '1:848828:ga:g', '1:856513:tc:t', '1:862822:g:gc', '1:875159:agccagtggacgccgacct:a', '1:880639:tc:t', '1:884549:cagag:c', '1:886831:g:gggtca', '1:895037:a:ag', '1:900717:cttat:c', '1:904868:atg:a', '1:905043:cat:c', '1:907170:ag:a', '1:919525:tagtc:t', '1:922305:g:gc', '1:925551:at:a', '1:928849:cag:c', '1:938709:ggggtggatcctgggctgca:g', '1:939491:tg:t', '1:940809:c:ca', '1:945135:g:ga', '1:948846:t:ta', '1:950113:gaagt:g', '1:962036:a:ag', '1:963466:acact:a', '1:975992:gacgtgggt:g', '1:978147:a:ag', '1:978603:cct:c', '1:994395:ag:a', '1:995305:ct:c', '...'] 2025-02-18 15:34:45,599 Filtered out 0 SNPs with "NEGATIVE GWAS P" (Filters out SNPs with negative GWAS P values) 2025-02-18 15:34:45,599 RS IDs = [] 2025-02-18 15:34:45,603 Filtered out 510337 SNPs in total (as the union of drops, this may be less than the total of all the per-filter drops) 2025-02-18 15:34:45,604 Additionally dropped 0 duplicate SNPs 2025-02-18 15:34:45,604 RS IDs = [] 2025-02-18 15:34:50,793 Number of SNPS in initial intersection of all sources: 5224321 2025-02-18 15:35:10,041 Standardizing reference alleles in summary statistics. 2025-02-18 15:35:19,861 Standardized to population: ('EUR', 'FEMALES_cIMT') 2025-02-18 15:35:19,863 Dropped 0 SNPs during reference allele standardization. 2025-02-18 15:35:19,864 RS IDs of drops during standardization: [] 2025-02-18 15:35:24,990 2025-02-18 15:35:25,013 Harmonized EUR FEMALES_cIMT mean chi squared: 1.0277880531787356 2025-02-18 15:35:25,033 Harmonized AFR FEMALES_cIMT mean chi squared: 1.0146716506139801 2025-02-18 15:35:25,033 2025-02-18 15:35:25,387 Converting ('EUR', 'FEMALES_cIMT') to standardized units 2025-02-18 15:35:25,459 Converting ('AFR', 'FEMALES_cIMT') to standardized units 2025-02-18 15:35:25,650 Running LD Score regression. 2025-02-18 15:35:25,650 Options = {'ld_fixed_opt': 'all_unconstrained', 'se_fixed_opt': 'offdiag_zero', 'int_fixed_opt': 'all_unconstrained', 'ld_scale_factor': None} 2025-02-18 15:35:26,750 Received Numpy error: divide by zero (1) 2025-02-18 15:35:27,027 Regression coefficients (LD): [[-2.11877785e-07 -4.30949835e-05] [-4.30949835e-05 -1.58572224e-05]] 2025-02-18 15:35:27,027 Regression coefficients (Intercept): [[ 2.07948130e-06 -3.34591361e-06] [-3.34591361e-06 3.59172410e-04]] 2025-02-18 15:35:27,028 Regression coefficients (SE^2): [[0.4876061 0. ] [0. 0.33165499]] 2025-02-18 15:35:27,028 Creating omega and sigma matrices. 2025-02-18 15:36:29,023 Average Omega (including dropped slices) = [[-9.60234438e-07 -1.11267192e-04] [-1.11267192e-04 -6.36156502e-05]] 2025-02-18 15:36:29,105 Average Sigma (including dropped slices) = [[ 3.13543520e-06 -3.34591361e-06] [-3.34591361e-06 5.03155489e-04]] 2025-02-18 15:36:29,108 Adjusted 0 SNPs to make omega positive semi-definite. 2025-02-18 15:36:29,109 RS IDs = [] 2025-02-18 15:36:29,112 Dropped 5224321 SNPs due to non-positive-semi-definiteness of omega. 2025-02-18 15:36:29,321 RS IDs = ['10:10000018:a:g', '10:100000625:a:g', '10:100000645:a:c', '10:100003242:t:g', '10:100003785:t:c', '10:100004360:g:a', '10:100004906:c:a', '10:100004996:g:a', '10:10000514:t:c', '10:100005282:c:t', '10:100007362:g:c', '10:100008436:g:a', '10:100010186:a:g', '10:10001085:g:a', '10:100011114:t:a', '10:100011970:g:a', '10:10001208:t:c', '10:100012739:a:g', '10:100012890:a:g', '10:100013244:a:c', '10:100013438:c:t', '10:100013563:c:t', '10:100013815:c:t', '10:100013977:a:t', '10:100015563:g:c', '10:100015603:g:t', '10:100016313:a:t', '10:100016339:c:t', '10:100017453:t:g', '10:100018238:c:t', '10:100018844:g:a', '10:100019039:g:t', '10:100020572:t:g', '10:100020880:c:t', '10:10002142:g:a', '10:100021533:a:g', '10:100021672:c:t', '10:10002186:g:a', '10:100021983:g:a', '10:100023208:c:t', '10:100023359:t:c', '10:100023614:a:g', '10:100023857:c:g', '10:100023957:t:g', '10:100024195:g:t', '10:100024848:g:a', '10:100025095:t:c', '10:100025816:g:a', '10:100025924:a:g', '10:100026127:t:a', '...'] 2025-02-18 15:36:29,324 Dropped 0 SNPs due to non-positive-definiteness of sigma. 2025-02-18 15:36:29,325 RS IDs = [] 2025-02-18 15:36:29,328 Dropped 5224321 total SNPs due to non-positive-(semi)-definiteness of omega / sigma. 2025-02-18 15:36:29,460 RS IDs = ['10:10000018:a:g', '10:100000625:a:g', '10:100000645:a:c', '10:100003242:t:g', '10:100003785:t:c', '10:100004360:g:a', '10:100004906:c:a', '10:100004996:g:a', '10:10000514:t:c', '10:100005282:c:t', '10:100007362:g:c', '10:100008436:g:a', '10:100010186:a:g', '10:10001085:g:a', '10:100011114:t:a', '10:100011970:g:a', '10:10001208:t:c', '10:100012739:a:g', '10:100012890:a:g', '10:100013244:a:c', '10:100013438:c:t', '10:100013563:c:t', '10:100013815:c:t', '10:100013977:a:t', '10:100015563:g:c', '10:100015603:g:t', '10:100016313:a:t', '10:100016339:c:t', '10:100017453:t:g', '10:100018238:c:t', '10:100018844:g:a', '10:100019039:g:t', '10:100020572:t:g', '10:100020880:c:t', '10:10002142:g:a', '10:100021533:a:g', '10:100021672:c:t', '10:10002186:g:a', '10:100021983:g:a', '10:100023208:c:t', '10:100023359:t:c', '10:100023614:a:g', '10:100023857:c:g', '10:100023957:t:g', '10:100024195:g:t', '10:100024848:g:a', '10:100025095:t:c', '10:100025816:g:a', '10:100025924:a:g', '10:100026127:t:a', '...'] 2025-02-18 15:36:29,460 Running main MAMA method. 2025-02-18 15:36:37,462 Preparing results for output. 2025-02-18 15:36:37,462 Population 0: ('EUR', 'FEMALES_cIMT') 2025-02-18 15:36:47,576 Received Numpy error: invalid value (8) 2025-02-18 15:36:47,576 Mean Chi^2 for ('EUR', 'FEMALES_cIMT') = nan 2025-02-18 15:36:47,576 Converting ('EUR', 'FEMALES_cIMT') from standardized units 2025-02-18 15:36:47,577 Population 1: ('AFR', 'FEMALES_cIMT') 2025-02-18 15:36:57,791 Received Numpy error: invalid value (8) 2025-02-18 15:36:57,791 Mean Chi^2 for ('AFR', 'FEMALES_cIMT') = nan 2025-02-18 15:36:57,791 Converting ('AFR', 'FEMALES_cIMT') from standardized units 2025-02-18 15:36:58,075 Final SNP count = 0 2025-02-18 15:36:58,751 Writing results to disk. 2025-02-18 15:36:58,751 ./cIMT_FEMALES_MAMA_EUR_FEMALES_cIMT.res 2025-02-18 15:36:58,759 ./cIMT_FEMALES_MAMA_AFR_FEMALES_cIMT.res 2025-02-18 15:36:58,761 Execution complete. — Reply to this email directly, view it on GitHub <#38>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFBUB5N233MEDEPXYQXYU432QNBEPAVCNFSM6AAAAABXL2FZSOVHI2DSMVQWIX3LMV43ASLTON2WKOZSHA3DANRWGQYTIOI> . You are receiving this because you are subscribed to this thread.Message ID: ***@***.***> [image: xEmz]*xEmz* created an issue (JonJala/mama#38) <#38> Hi, I got MAMA to work. But now I have an issue I can't seem to figure out. I keep getting NaN output because something goes wrong with calculating the omega. When I only run European data I do get output, but when I analyze it with African and Latin data, it seems to drop SNPs. What could be the issue? Verbose log file: 2025-02-18 15:29:43,197 <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> <> <> MAMA: Multi-Ancestry Meta-Analysis <> Version: 1.0.0 <> (C) 2020 Social Science Genetic Association Consortium (SSGAC) <> MIT License <> <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> <> Software-related correspondence: ***@***.*** <> All other correspondence: ***@***.*** <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> 2025-02-18 15:29:43,197 See full log at: /hpc/dhl_ec/esmulders/mama/cIMT_FEMALES_MAMA.log 2025-02-18 15:29:43,197 Program executed via: ./mama.py \ --sumstats /hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_EUR/females_eur/META/input/CHARGE_cIMT_FEMALES_EUR.mama.b37.gwaslab.qc.txt.gz,EUR,FEMALES_cIMT /hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_AFR/females_afr/META/input/CHARGE_cIMT_FEMALES_AFR.mama.b37.gwaslab.qc.txt.gz,AFR,FEMALES_cIMT \ --ld-scores /hpc/dhl_ec/esmulders/references/MAMA/1kgp3v5.hg19.split_norm_af.AFR_AMR_EUR.FEMALES_maf0_01_geno0_10.ldscore.ldscores.txt \ --out ./cIMT_FEMALES_MAMA \ --verbose \ --use-standardized-units \ --allow-palindromic-snps \ --input-sep \t 2025-02-18 15:29:43,197 Printing Pandas' version summary: 2025-02-18 15:29:43,340 INSTALLED VERSIONS commit : 478d340667831908b5b4bf09a2787a11a14560c9 python : 3.9.21.final.0 python-bits : 64 OS : Linux OS-release : 4.18.0-553.30.1.el8_10.x86_64 Version : #1 <#1> SMP Tue Nov 26 18:56:25 UTC 2024 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 2.0.0 numpy : 1.23.0 pytz : 2025.1 dateutil : 2.9.0.post0 setuptools : 75.8.0 pip : 25.0.1 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None brotli : None fastparquet : None fsspec : None gcsfs : None matplotlib : None numba : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : 1.11.1 snappy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None zstandard : None tzdata : 2025.1 qtpy : None pyqt5 : None 2025-02-18 15:29:43,341 Program was called with the following arguments: {'use_standardized_units': True, 'allow_palindromic_snps': True, 'ld_scores': ['/hpc/dhl_ec/esmulders/references/MAMA/1kgp3v5.hg19.split_norm_af.AFR_AMR_EUR.FEMALES_maf0_01_geno0_10.ldscore.ldscores.txt'], 'sumstats': [('/hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_EUR/females_eur/META/input/CHARGE_cIMT_FEMALES_EUR.mama.b37.gwaslab.qc.txt.gz', 'EUR', 'FEMALES_cIMT'), ('/hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_AFR/females_afr/META/input/CHARGE_cIMT_FEMALES_AFR.mama.b37.gwaslab.qc.txt.gz', 'AFR', 'FEMALES_cIMT')], 'out': './cIMT_FEMALES_MAMA', 'input_sep': '\t', 'verbose': True} 2025-02-18 15:29:43,401 Regex map = {'SNP': '.*SNP.*|.*RS.*', 'BP': '.*BP.*|.*POS.*', 'CHR': '. *CHR.*', 'BETA': '.*BETA.*', 'FREQ': '.*FREQ.*|.*FRQ.*|.*AF', 'SE': '. *SE.*', 'A1': '.*A1.*|.*MAJOR.*|.*EFFECT.ALL.|REF.*', 'A2': '.*A2.*|. *MINOR.*|.*OTHER.ALL.|ALT.*', 'P': 'P|P.*VAL.*', 'INFO': 'INFO', 'N': 'N'} 2025-02-18 15:29:43,406 Filter map = {'NO NAN': (<function at 0x7f18d4eceaf0>, "Filters out SNPs with any NaN values in required columns {'FREQ', 'BETA', 'CHR', 'P', 'SE', 'BP', 'A1', 'SNP', 'A2'}"), 'FREQ BOUNDS': (<function create_freq_filter.. at 0x7f18cd9d03a0>, 'Filters out SNPs with FREQ values outside of [0.01, 0.99]'), 'SE BOUNDS': (<function at 0x7f18cd9d0430>, 'Filters out SNPs with non-positive SE values'), 'CHR VALUES': (<function create_chr_filter.. at 0x7f18cd9d04c0>, "Filters out SNPs with listed chromosomes not in ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', 'X', 'Y']"), 'DUPLICATE ALLELE SNPS': (<function at 0x7f18cd9d0550>, 'Filters out SNPs with major allele = minor allele'), 'INVALID ALLELES': (<function at 0x7f18cd9d0670>, "Filters out SNPs with alleles not in {'T', 'G', 'C', 'A'}"), 'NEGATIVE GWAS P': (<function at 0x7f18cd9d0700>, 'Filters out SNPs with negative GWAS P values')} 2025-02-18 15:29:43,406 Regression coeffient option (LD) = all_unconstrained 2025-02-18 15:29:43,406 Regression coeffient option (LD Scale) = None 2025-02-18 15:29:43,406 Regression coeffient option (Intercept) = all_unconstrained 2025-02-18 15:29:43,406 Regression coeffient option (SE^2) = offdiag_zero 2025-02-18 15:29:43,406 Reading in and running QC on LD Scores 2025-02-18 15:29:43,406 List of files: ['/hpc/dhl_ec/esmulders/references/MAMA/1kgp3v5.hg19.split_norm_af.AFR_AMR_EUR.FEMALES_maf0_01_geno0_10.ldscore.ldscores.txt'] 2025-02-18 15:29:43,406 Reading in LD Scores file: /hpc/dhl_ec/esmulders/references/MAMA/1kgp3v5.hg19.split_norm_af.AFR_AMR_EUR.FEMALES_maf0_01_geno0_10.ldscore.ldscores.txt 2025-02-18 15:31:45,171 Reading in summary statistics. 2025-02-18 15:31:45,172 2025-02-18 15:31:45,172 Reading in ('EUR', 'FEMALES_cIMT') sumstats file: /hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_EUR/females_eur/META/input/CHARGE_cIMT_FEMALES_EUR.mama.b37.gwaslab.qc.txt.gz 2025-02-18 15:32:48,331 Running QC on ('EUR', 'FEMALES_cIMT') summary statistics 2025-02-18 15:32:48,332 Column mapping = {'BP': 'BP', 'Chr': 'CHR', 'rsID': 'SNP', 'A1': 'A1', 'A2': 'A2', 'EAF': 'FREQ', 'BETA': 'BETA', 'P': 'P', 'SE': 'SE', 'N': 'N'} 2025-02-18 15:32:48,333 First set of rows from initial reading of summary stats: BP Chr rsID A1 A2 EAF BETA P SE N 0 10177 1 1:10177:A:AC AC A 0.401 -0.0005 0.81272 0.0022 11540 1 10352 1 1:10352:T:TA TA T 0.403 0.0004 0.84544 0.0023 11113 2 14599 1 1:14599:T:A A T 0.189 -0.0023 0.41643 0.0029 10585 3 14930 1 1:14930:A:G G A 0.539 0.0034 0.12661 0.0022 10935 4 15903 1 1:15903:G:GC GC G 0.412 -0.0035 0.10523 0.0022 11659 2025-02-18 15:32:48,345 Initial number of SNPs / rows = 8986320 ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� '1:15903:g:gc', '1:63735:ccta:c', '1:129010:aatg:a', '1:249275:g:gt', '1:251627:ac:a', '1:255923:g:gtc', '1:540540:gc:g', '1:612688:tctc:t', '1:668394:ag:a', '1:701779:gaata:g', '1:713131:a:at', '1:732134:t:ta', '1:733581:ca:c', '1:745642:ac:a', '1:746211:a:ag', '1:747753:tgc:t', '1:752307:at:a', '1:760811:ctctt:c', '1:761957:a:at', '1:765155:ttgac:t', '1:769138:cat:c', '1:787069:cag:c', '1:789513:ga:g', '1:797126:gtaat:g', '1:821325:acagt:a', '1:832297:ctg:c', '1:832960:at:a', '1:833172:t:tcgaa', '1:834637:ac:a', '1:837553:ggtgt:g', '1:838329:g:gc', '1:842057:a:aaactcagctgcctctccccttc', '1:842387:a:acc', '1:846600:ct:c', '1:848654:tg:t', '1:849863:g:gactgcccagctc', '1:850425:g:ggtcc', '1:852011:agagccggccct:a', '1:858691:tg:t', '1:868928:a:ag', '1:871042:c:ca', '1:874950:t:tccctggaggacc', '1:875159:agccagtggacgccgacct:a', '1:880639:tc:t', '1:886179:ca:c', '1:893461:tc:t', '1:895755:a:ag', '1:900717:cttat:c', '...'] 2025-02-18 15:33:06,577 Filtered out 0 SNPs with "NEGATIVE GWAS P" (Filters out SNPs with negative GWAS P values) 2025-02-18 15:33:06,577 RS IDs = [] 2025-02-18 15:33:06,580 Filtered out 564607 SNPs in total (as the union of drops, this may be less than the total of all the per-filter drops) 2025-02-18 15:33:06,580 Additionally dropped 0 duplicate SNPs 2025-02-18 15:33:06,580 RS IDs = [] 2025-02-18 15:33:06,592 2025-02-18 15:33:06,592 Reading in ('AFR', 'FEMALES_cIMT') sumstats file: /hpc/dhl_ec/svanderlaan/projects/consortia/CHARGE_cIMT_Sex/SECOND_ROUND/CHARGE_cIMT_FEMALES_AFR/females_afr/META/input/CHARGE_cIMT_FEMALES_AFR.mama.b37.gwaslab.qc.txt.gz 2025-02-18 15:34:22,988 Running QC on ('AFR', 'FEMALES_cIMT') summary statistics 2025-02-18 15:34:22,989 Column mapping = {'BP': 'BP', 'Chr': 'CHR', 'rsID': 'SNP', 'A1': 'A1', 'A2': 'A2', 'EAF': 'FREQ', 'BETA': 'BETA', 'P': 'P', 'SE': 'SE', 'N': 'N'} 2025-02-18 15:34:22,990 First set of rows from initial reading of summary stats: BP Chr rsID A1 A2 EAF BETA P SE N 0 662622 1 1:662622:G:A A G 0.202 0.0882 0.057074 0.0463 1148 1 693731 1 1:693731:A:G G A 0.196 0.0906 0.052977 0.0468 1165 2 693823 1 1:693823:G:C C G 0.211 0.0694 0.131840 0.0461 1174 3 703942 1 1:703942:G:C C G 0.189 0.0234 0.615080 0.0466 1292 4 705452 1 1:705452:T:A A T 0.193 0.0238 0.607910 0.0463 1299 2025-02-18 15:34:22,998 Initial number of SNPs / rows = 10011608 2025-02-18 15:34:45,599 Filtered out 0 SNPs with "NO NAN" (Filters out SNPs with any NaN values in required columns {'FREQ', 'BETA', 'CHR', 'P', 'SE', 'BP', 'A1', 'SNP', 'A2'}) 2025-02-18 15:34:45,599 RS IDs = [] 2025-02-18 15:34:45,599 Filtered out 0 SNPs with "FREQ BOUNDS" (Filters out SNPs with FREQ values outside of [0.01, 0.99]) 2025-02-18 15:34:45,599 RS IDs = [] 2025-02-18 15:34:45,599 Filtered out 0 SNPs with "SE BOUNDS" (Filters out SNPs with non-positive SE values) 2025-02-18 15:34:45,599 RS IDs = [] 2025-02-18 15:34:45,599 Filtered out 0 SNPs with "CHR VALUES" (Filters out SNPs with listed chromosomes not in ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', 'X', 'Y']) 2025-02-18 15:34:45,599 RS IDs = [] 2025-02-18 15:34:45,599 Filtered out 0 SNPs with "DUPLICATE ALLELE SNPS" (Filters out SNPs with major allele = minor allele) 2025-02-18 15:34:45,599 RS IDs = [] 2025-02-18 15:34:45,599 Filtered out 510337 SNPs with "INVALID ALLELES" (Filters out SNPs with alleles not in {'T', 'G', 'C', 'A'}) 2025-02-18 15:34:45,599 RS IDs = ['1:723738:act:a', '1:746037:tac:t', '1:752509:ca:c', '1:764261:tca:t', '1:769138:cat:c', '1:774546:tgaga:t', '1:779797:g:gctcc', '1:780347:tttaa:t', '1:781057:tc:t', '1:789513:ga:g', '1:819121:c:cat', '1:821325:acagt:a', '1:821862:cacagcagctgtgctgtgtt:c', '1:827712:t:ta', '1:832297:ctg:c', '1:833172:t:tcgaa', '1:834637:ac:a', '1:837553:ggtgt:g', '1:838329:g:gc', '1:846600:ct:c', '1:848654:tg:t', '1:848828:ga:g', '1:856513:tc:t', '1:862822:g:gc', '1:875159:agccagtggacgccgacct:a', '1:880639:tc:t', '1:884549:cagag:c', '1:886831:g:gggtca', '1:895037:a:ag', '1:900717:cttat:c', '1:904868:atg:a', '1:905043:cat:c', '1:907170:ag:a', '1:919525:tagtc:t', '1:922305:g:gc', '1:925551:at:a', '1:928849:cag:c', '1:938709:ggggtggatcctgggctgca:g', '1:939491:tg:t', '1:940809:c:ca', '1:945135:g:ga', '1:948846:t:ta', '1:950113:gaagt:g', '1:962036:a:ag', '1:963466:acact:a', '1:975992:gacgtgggt:g', '1:978147:a:ag', '1:978603:cct:c', '1:994395:ag:a', '1:995305:ct:c', '...'] 2025-02-18 15:34:45,599 Filtered out 0 SNPs with "NEGATIVE GWAS P" (Filters out SNPs with negative GWAS P values) 2025-02-18 15:34:45,599 RS IDs = [] 2025-02-18 15:34:45,603 Filtered out 510337 SNPs in total (as the union of drops, this may be less than the total of all the per-filter drops) 2025-02-18 15:34:45,604 Additionally dropped 0 duplicate SNPs 2025-02-18 15:34:45,604 RS IDs = [] 2025-02-18 15:34:50,793 Number of SNPS in initial intersection of all sources: 5224321 2025-02-18 15:35:10,041 Standardizing reference alleles in summary statistics. 2025-02-18 15:35:19,861 Standardized to population: ('EUR', 'FEMALES_cIMT') 2025-02-18 15:35:19,863 Dropped 0 SNPs during reference allele standardization. 2025-02-18 15:35:19,864 RS IDs of drops during standardization: [] 2025-02-18 15:35:24,990 2025-02-18 15:35:25,013 Harmonized EUR FEMALES_cIMT mean chi squared: 1.0277880531787356 2025-02-18 15:35:25,033 Harmonized AFR FEMALES_cIMT mean chi squared: 1.0146716506139801 2025-02-18 15:35:25,033 2025-02-18 15:35:25,387 Converting ('EUR', 'FEMALES_cIMT') to standardized units 2025-02-18 15:35:25,459 Converting ('AFR', 'FEMALES_cIMT') to standardized units 2025-02-18 15:35:25,650 Running LD Score regression. 2025-02-18 15:35:25,650 Options = {'ld_fixed_opt': 'all_unconstrained', 'se_fixed_opt': 'offdiag_zero', 'int_fixed_opt': 'all_unconstrained', 'ld_scale_factor': None} 2025-02-18 15:35:26,750 Received Numpy error: divide by zero (1) 2025-02-18 15:35:27,027 Regression coefficients (LD): [[-2.11877785e-07 -4.30949835e-05] [-4.30949835e-05 -1.58572224e-05]] 2025-02-18 15:35:27,027 Regression coefficients (Intercept): [[ 2.07948130e-06 -3.34591361e-06] [-3.34591361e-06 3.59172410e-04]] 2025-02-18 15:35:27,028 Regression coefficients (SE^2): [[0.4876061 0. ] [0. 0.33165499]] 2025-02-18 15:35:27,028 Creating omega and sigma matrices. 2025-02-18 15:36:29,023 Average Omega (including dropped slices) = [[-9.60234438e-07 -1.11267192e-04] [-1.11267192e-04 -6.36156502e-05]] 2025-02-18 15:36:29,105 Average Sigma (including dropped slices) = [[ 3.13543520e-06 -3.34591361e-06] [-3.34591361e-06 5.03155489e-04]] 2025-02-18 15:36:29,108 Adjusted 0 SNPs to make omega positive semi-definite. 2025-02-18 15:36:29,109 RS IDs = [] 2025-02-18 15:36:29,112 Dropped 5224321 SNPs due to non-positive-semi-definiteness of omega. 2025-02-18 15:36:29,321 RS IDs = ['10:10000018:a:g', '10:100000625:a:g', '10:100000645:a:c', '10:100003242:t:g', '10:100003785:t:c', '10:100004360:g:a', '10:100004906:c:a', '10:100004996:g:a', '10:10000514:t:c', '10:100005282:c:t', '10:100007362:g:c', '10:100008436:g:a', '10:100010186:a:g', '10:10001085:g:a', '10:100011114:t:a', '10:100011970:g:a', '10:10001208:t:c', '10:100012739:a:g', '10:100012890:a:g', '10:100013244:a:c', '10:100013438:c:t', '10:100013563:c:t', '10:100013815:c:t', '10:100013977:a:t', '10:100015563:g:c', '10:100015603:g:t', '10:100016313:a:t', '10:100016339:c:t', '10:100017453:t:g', '10:100018238:c:t', '10:100018844:g:a', '10:100019039:g:t', '10:100020572:t:g', '10:100020880:c:t', '10:10002142:g:a', '10:100021533:a:g', '10:100021672:c:t', '10:10002186:g:a', '10:100021983:g:a', '10:100023208:c:t', '10:100023359:t:c', '10:100023614:a:g', '10:100023857:c:g', '10:100023957:t:g', '10:100024195:g:t', '10:100024848:g:a', '10:100025095:t:c', '10:100025816:g:a', '10:100025924:a:g', '10:100026127:t:a', '...'] 2025-02-18 15:36:29,324 Dropped 0 SNPs due to non-positive-definiteness of sigma. 2025-02-18 15:36:29,325 RS IDs = [] 2025-02-18 15:36:29,328 Dropped 5224321 total SNPs due to non-positive-(semi)-definiteness of omega / sigma. 2025-02-18 15:36:29,460 RS IDs = ['10:10000018:a:g', '10:100000625:a:g', '10:100000645:a:c', '10:100003242:t:g', '10:100003785:t:c', '10:100004360:g:a', '10:100004906:c:a', '10:100004996:g:a', '10:10000514:t:c', '10:100005282:c:t', '10:100007362:g:c', '10:100008436:g:a', '10:100010186:a:g', '10:10001085:g:a', '10:100011114:t:a', '10:100011970:g:a', '10:10001208:t:c', '10:100012739:a:g', '10:100012890:a:g', '10:100013244:a:c', '10:100013438:c:t', '10:100013563:c:t', '10:100013815:c:t', '10:100013977:a:t', '10:100015563:g:c', '10:100015603:g:t', '10:100016313:a:t', '10:100016339:c:t', '10:100017453:t:g', '10:100018238:c:t', '10:100018844:g:a', '10:100019039:g:t', '10:100020572:t:g', '10:100020880:c:t', '10:10002142:g:a', '10:100021533:a:g', '10:100021672:c:t', '10:10002186:g:a', '10:100021983:g:a', '10:100023208:c:t', '10:100023359:t:c', '10:100023614:a:g', '10:100023857:c:g', '10:100023957:t:g', '10:100024195:g:t', '10:100024848:g:a', '10:100025095:t:c', '10:100025816:g:a', '10:100025924:a:g', '10:100026127:t:a', '...'] 2025-02-18 15:36:29,460 Running main MAMA method. 2025-02-18 15:36:37,462 Preparing results for output. 2025-02-18 15:36:37,462 Population 0: ('EUR', 'FEMALES_cIMT') 2025-02-18 15:36:47,576 Received Numpy error: invalid value (8) 2025-02-18 15:36:47,576 Mean Chi^2 for ('EUR', 'FEMALES_cIMT') = nan 2025-02-18 15:36:47,576 Converting ('EUR', 'FEMALES_cIMT') from standardized units 2025-02-18 15:36:47,577 Population 1: ('AFR', 'FEMALES_cIMT') 2025-02-18 15:36:57,791 Received Numpy error: invalid value (8) 2025-02-18 15:36:57,791 Mean Chi^2 for ('AFR', 'FEMALES_cIMT') = nan 2025-02-18 15:36:57,791 Converting ('AFR', 'FEMALES_cIMT') from standardized units 2025-02-18 15:36:58,075 Final SNP count = 0 2025-02-18 15:36:58,751 Writing results to disk. 2025-02-18 15:36:58,751 ./cIMT_FEMALES_MAMA_EUR_FEMALES_cIMT.res 2025-02-18 15:36:58,759 ./cIMT_FEMALES_MAMA_AFR_FEMALES_cIMT.res 2025-02-18 15:36:58,761 Execution complete. — Reply to this email directly, view it on GitHub <#38>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFBUB5N233MEDEPXYQXYU432QNBEPAVCNFSM6AAAAABXL2FZSOVHI2DSMVQWIX3LMV43ASLTON2WKOZSHA3DANRWGQYTIOI> . You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>

[xEmz]

Yes, I find that when I also run Africans and Latin Americans separately, I also get output. So the issue seems different LD patterns, allele frequencies, or effect sizes across groups?

[paturley]

Not due to different patterns, but just the funny issue about negative LD score coefficients and negative LD scores. When you run MAMA on a single ancestry, if you get a negative LD score coefficient (like you do in your data) it will keep the small number of SNPs with negative LD scores. But if you use more than one ancestry, it will only keep SNPs that have a negative LD score in both ancestries (which is probably an empty set). An easy way to confirm this is to look at the average LD score of the SNPs for which single-ancestry MAMA has output.

…

On Wed, Feb 19, 2025 at 3:29 AM Emma Smulders ***@***.***> wrote: Yes, I find that when I also run Africans and Latin Americans separately, I also get output. So the issue seems different LD patterns, allele frequencies, or effect sizes across groups? — Reply to this email directly, view it on GitHub <#38 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFBUB5JJRFWXZ4GJHWM2ODL2QQ6G3AVCNFSM6AAAAABXL2FZSOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDMNRXHA4TSOJRGY> . You are receiving this because you commented.Message ID: ***@***.***> [image: xEmz]*xEmz* left a comment (JonJala/mama#38) <#38 (comment)> Yes, I find that when I also run Africans and Latin Americans separately, I also get output. So the issue seems different LD patterns, allele frequencies, or effect sizes across groups? — Reply to this email directly, view it on GitHub <#38 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFBUB5JJRFWXZ4GJHWM2ODL2QQ6G3AVCNFSM6AAAAABXL2FZSOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDMNRXHA4TSOJRGY> . You are receiving this because you commented.Message ID: ***@***.***>

[xEmz]

Ah yes, I see:

Running LD Score regression.
2025-02-18 16:37:02,909 	Options = {'ld_fixed_opt': 'all_unconstrained', 'se_fixed_opt': 'offdiag_zero', 'int_fixed_opt': 'all_unconstrained', 'ld_scale_factor': None}
2025-02-18 16:37:02,984 Regression coefficients (LD):
**[[-3.92081095e-06]]**
2025-02-18 16:37:02,985 Regression coefficients (Intercept):
[[-1.0820655e-06]]
2025-02-18 16:37:02,986 Regression coefficients (SE^2):
[[1.03421788]]

What would you recommend me to do? I made LD scores using MAMA per chromosome. But considering it won't take, I need to find another way, so I'm stuck unfortunately.

My code:

SEX="FEMALES"

for CHR in $(seq 1 23); do
python3 ./mama_ldscores.py \
--gendata "/hpc/dhl_ec/esmulders/references/MAMA/1kGp3v5.ALL.split_norm_af.1kgp3v5.hg19.EUR.${SEX}_maf0_01_geno0_10.chr${CHR},EUR" "/hpc/dhl_ec/esmulders/references/MAMA/1kGp3v5.ALL.split_norm_af.1kgp3v5.hg19.AFR.${SEX}_maf0_01_geno0_10.chr${CHR},AFR" "/hpc/dhl_ec/esmulders/references/MAMA/1kGp3v5.ALL.split_norm_af.1kgp3v5.hg19.AMR.${SEX}_maf0_01_geno0_10.chr${CHR},AMR" \
--window-bp 1000 \
--verbose \
--use-standardized-units \
--out "/hpc/dhl_ec/esmulders/references/MAMA/1kgp3v5.hg19.split_norm_af.AFR_AMR_EUR.${SEX}_maf0_01_geno0_10.ldscore.chr${CHR}"
		done

[paturley]

Yeah, I don't think that MAMA is well-suited for your case. I'm not sure what the right call would be for you. You could just do a raw meta-analysis of the data, but then the resulting summary statistics would not correspond to any particular ancestry and they couldn't be used in follow-up analyses that require an LD reference panel. But if you are just interested in finding SNPs that are associated with the trait, then the p-value for the meta-analysis would be correctly calibrated against the null that the SNP is not causal for either trait.

…

On Fri, Feb 21, 2025 at 9:07 AM Emma Smulders ***@***.***> wrote: Ah yes, I see: Running LD Score regression. 2025-02-18 16:37:02,909 Options = {'ld_fixed_opt': 'all_unconstrained', 'se_fixed_opt': 'offdiag_zero', 'int_fixed_opt': 'all_unconstrained', 'ld_scale_factor': None} 2025-02-18 16:37:02,984 Regression coefficients (LD): **[[-3.92081095e-06]]** 2025-02-18 16:37:02,985 Regression coefficients (Intercept): [[-1.0820655e-06]] 2025-02-18 16:37:02,986 Regression coefficients (SE^2): [[1.03421788]] What would you recommend me to do? I made LD scores using MAMA per chromosome. But considering it won't take, I need to find another way, so I'm stuck unfortunately. My code: SEX="FEMALES" for CHR in $(seq 1 23); do python3 ./mama_ldscores.py \ --gendata "/hpc/dhl_ec/esmulders/references/MAMA/1kGp3v5.ALL.split_norm_af.1kgp3v5.hg19.EUR.${SEX}_maf0_01_geno0_10.chr${CHR},EUR" "/hpc/dhl_ec/esmulders/references/MAMA/1kGp3v5.ALL.split_norm_af.1kgp3v5.hg19.AFR.${SEX}_maf0_01_geno0_10.chr${CHR},AFR" "/hpc/dhl_ec/esmulders/references/MAMA/1kGp3v5.ALL.split_norm_af.1kgp3v5.hg19.AMR.${SEX}_maf0_01_geno0_10.chr${CHR},AMR" \ --window-bp 1000 \ --verbose \ --use-standardized-units \ --out "/hpc/dhl_ec/esmulders/references/MAMA/1kgp3v5.hg19.split_norm_af.AFR_AMR_EUR.${SEX}_maf0_01_geno0_10.ldscore.chr${CHR}" done — Reply to this email directly, view it on GitHub <#38 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFBUB5IDUNLLNHNNNWGIIMT2Q4XKNAVCNFSM6AAAAABXL2FZSOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDMNZUGY2DOMJWGM> . You are receiving this because you commented.Message ID: ***@***.***> [image: xEmz]*xEmz* left a comment (JonJala/mama#38) <#38 (comment)> Ah yes, I see: Running LD Score regression. 2025-02-18 16:37:02,909 Options = {'ld_fixed_opt': 'all_unconstrained', 'se_fixed_opt': 'offdiag_zero', 'int_fixed_opt': 'all_unconstrained', 'ld_scale_factor': None} 2025-02-18 16:37:02,984 Regression coefficients (LD): **[[-3.92081095e-06]]** 2025-02-18 16:37:02,985 Regression coefficients (Intercept): [[-1.0820655e-06]] 2025-02-18 16:37:02,986 Regression coefficients (SE^2): [[1.03421788]] What would you recommend me to do? I made LD scores using MAMA per chromosome. But considering it won't take, I need to find another way, so I'm stuck unfortunately. My code: SEX="FEMALES" for CHR in $(seq 1 23); do python3 ./mama_ldscores.py \ --gendata "/hpc/dhl_ec/esmulders/references/MAMA/1kGp3v5.ALL.split_norm_af.1kgp3v5.hg19.EUR.${SEX}_maf0_01_geno0_10.chr${CHR},EUR" "/hpc/dhl_ec/esmulders/references/MAMA/1kGp3v5.ALL.split_norm_af.1kgp3v5.hg19.AFR.${SEX}_maf0_01_geno0_10.chr${CHR},AFR" "/hpc/dhl_ec/esmulders/references/MAMA/1kGp3v5.ALL.split_norm_af.1kgp3v5.hg19.AMR.${SEX}_maf0_01_geno0_10.chr${CHR},AMR" \ --window-bp 1000 \ --verbose \ --use-standardized-units \ --out "/hpc/dhl_ec/esmulders/references/MAMA/1kgp3v5.hg19.split_norm_af.AFR_AMR_EUR.${SEX}_maf0_01_geno0_10.ldscore.chr${CHR}" done — Reply to this email directly, view it on GitHub <#38 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFBUB5IDUNLLNHNNNWGIIMT2Q4XKNAVCNFSM6AAAAABXL2FZSOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDMNZUGY2DOMJWGM> . You are receiving this because you commented.Message ID: ***@***.***>