Missing SNPs in MTAG summary results

[JuRaGa]

Dear Omeed,

I have two phenotypes that are moderately correlated (rho = 0.6). Therefore, I used MTAG to boost the power of my single-GWASs. However, when inspecting the results, I see that some of the original SNPs available in the single-GWAS summary statistics are missing from the MTAG results for both phenotypes.

I have checked for allele inconsistency between both single-GWASs and this is not the case.

I was wondering if you could help me with this issue?

Many thanks,
Julia

[paturley]

Hi Julia, There are many filters that MTAG imposes beyond allelic consistency. It's hard to diagnose the problem without seeing the data. Can you share the row of the summary statistic file corresponding to a SNP that is present in both GWAS files and not present in the MTAG file? Best, Patrick

…

On Tue, Dec 18, 2018 at 11:07 AM Julia-Ramirez ***@***.***> wrote: Dear Omeed, I have two phenotypes that are moderately correlated (rho = 0.6). Therefore, I used MTAG to boost the power of my single-GWASs. However, when inspecting the results, I see that some of the original SNPs available in the single-GWAS summary statistics are missing from the MTAG results for both phenotypes. I have checked for allele inconsistency between both single-GWASs and this is not the case. I was wondering if you could help me with this issue? Many thanks, Julia — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#59>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AUNA9UNWjVk-dWJ1aRku96JoGhHa4o3uks5u6RKwgaJpZM4ZYmoF> .

[JuRaGa]

Hi Patrick, Thanks for your quick reply. This is the row from the single-GWAS summary statistics for phenotype 1: snpid CHR BP a1 a2 A1FREQ pval n z 13:74505951_CT_C 13 74505951 CT C 0.365313 9.6e-14 49711.36 7.446459 This is the row from the single-GWAS summary statistics for phenotype 2: snpid CHR BP a1 a2 A1FREQ pval n z 13:74505951_CT_C 13 74505951 CT C 0.365748 0.012 49346.31 2.51686390581907 And then this SNP is missing from the MTAG summary statistics for both phenotypes. I also have examples for SNVs that are not indels, so this doesn't only happen with indels. Thanks very much for your help, Julia On 18/12/2018 21:39, paturley wrote: Hi Julia, There are many filters that MTAG imposes beyond allelic consistency. It's hard to diagnose the problem without seeing the data. Can you share the row of the summary statistic file corresponding to a SNP that is present in both GWAS files and not present in the MTAG file? Best, Patrick

On Tue, Dec 18, 2018 at 11:07 AM Julia-Ramirez ***@***.******@***.***> wrote: Dear Omeed, I have two phenotypes that are moderately correlated (rho = 0.6). Therefore, I used MTAG to boost the power of my single-GWASs. However, when inspecting the results, I see that some of the original SNPs available in the single-GWAS summary statistics are missing from the MTAG results for both phenotypes. I have checked for allele inconsistency between both single-GWASs and this is not the case. I was wondering if you could help me with this issue? Many thanks, Julia — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#59>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AUNA9UNWjVk-dWJ1aRku96JoGhHa4o3uks5u6RKwgaJpZM4ZYmoF><https://github.com/notifications/unsubscribe-auth/AUNA9UNWjVk-dWJ1aRku96JoGhHa4o3uks5u6RKwgaJpZM4ZYmoF> .

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub<#59 (comment)>, or mute the thread<https://github.com/notifications/unsubscribe-auth/Aij-i3BmU8G-zy5Ny3AqG89saG2VDj-Uks5u6WCqgaJpZM4ZYmoF>. [https://ipmcdn.avast.com/images/icons/icon-envelope-tick-round-orange-animated-no-repeat-v1.gif]<https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> Virus-free. www.avast.com<https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient>

[paturley]

MTAG includes a number of filters by default, including any filters imposed by ldsc. (Which includes indels, I believe.) Have you looked over the documentation to confirm that there are not filters in place that cause you to lose those SNPs?

…

On Wed, Dec 19, 2018, 3:28 AM Julia-Ramirez ***@***.*** wrote: Hi Patrick, Thanks for your quick reply. This is the row from the single-GWAS summary statistics for phenotype 1: snpid CHR BP a1 a2 A1FREQ pval n z 13:74505951_CT_C 13 74505951 CT C 0.365313 9.6e-14 49711.36 7.446459 This is the row from the single-GWAS summary statistics for phenotype 2: snpid CHR BP a1 a2 A1FREQ pval n z 13:74505951_CT_C 13 74505951 CT C 0.365748 0.012 49346.31 2.51686390581907 And then this SNP is missing from the MTAG summary statistics for both phenotypes. I also have examples for SNVs that are not indels, so this doesn't only happen with indels. Thanks very much for your help, Julia On 18/12/2018 21:39, paturley wrote: Hi Julia, There are many filters that MTAG imposes beyond allelic consistency. It's hard to diagnose the problem without seeing the data. Can you share the row of the summary statistic file corresponding to a SNP that is present in both GWAS files and not present in the MTAG file? Best, Patrick On Tue, Dec 18, 2018 at 11:07 AM Julia-Ramirez ***@***.*** ***@***.***> wrote: > Dear Omeed, > > I have two phenotypes that are moderately correlated (rho = 0.6). > Therefore, I used MTAG to boost the power of my single-GWASs. However, when > inspecting the results, I see that some of the original SNPs available in > the single-GWAS summary statistics are missing from the MTAG results for > both phenotypes. > > I have checked for allele inconsistency between both single-GWASs and this > is not the case. > > I was wondering if you could help me with this issue? > > Many thanks, > Julia > > — > You are receiving this because you are subscribed to this thread. > Reply to this email directly, view it on GitHub > <#59>< #59>, or mute the thread > < https://github.com/notifications/unsubscribe-auth/AUNA9UNWjVk-dWJ1aRku96JoGhHa4o3uks5u6RKwgaJpZM4ZYmoF >< https://github.com/notifications/unsubscribe-auth/AUNA9UNWjVk-dWJ1aRku96JoGhHa4o3uks5u6RKwgaJpZM4ZYmoF> > . > — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub< #59 (comment)>, or mute the thread< https://github.com/notifications/unsubscribe-auth/Aij-i3BmU8G-zy5Ny3AqG89saG2VDj-Uks5u6WCqgaJpZM4ZYmoF>. [ https://ipmcdn.avast.com/images/icons/icon-envelope-tick-round-orange-animated-no-repeat-v1.gif ]< https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> Virus-free. www.avast.com< https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#59 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AUNA9VwFVKSmqro2ux5f3A0iujwiNjnYks5u6fiogaJpZM4ZYmoF> .

[huilisabrina]

Hi @julia-ramirez ,

MTAG adopts some of the basic filters used in the munging step of ldsc, one of which is dropping multiallelic variants.

For the specific SNP you mentioned, it was most likely filtered at this step: (you can check this line in your log file. The [x] in your case is probably non-zero.

Removed [x] variants that were not SNPs. Note: strand ambiguous SNPs were not dropped.

However, if you really want to retain those multi-allelic variants, there is a way to do so. You can try modifying your local version of the MTAG code by turning on one of the (rarely-used) arguments it passes on to ldsc: You can change no_alleles=False to no_alleles=True in line [154] and [156] of mtag.py. I just tested it and this works for me.

Multiallelic variants can be retained in the MTAG output this way. For general purpose however, we won't change the current default option (i.e. filtering out multiallelic variants), but maybe we can add an option for the users in the future.

Let me know if this tweak works for you, or I can send you the modified copy via email.

Best,
Hui

[JuRaGa]

Dear Hui, Thank you very much for your reply. Does this multi-allelic filter apply to the effect allele only or also to the other allele? This could explain why these SNPs are filtered out. Also, thank you very much for the advice with the flag. We will try this and let you know. Merry Christmas and happy new year! Julia On 28/12/2018 23:37, huilisabrina wrote: Hi @julia-ramirez<https://github.com/Julia-Ramirez> , MTAG adopts some of the basic filters used in the munging step of ldsc<https://github.com/bulik/ldsc/wiki/Heritability-and-Genetic-Correlation#reformatting-summary-statistics>, one of which is dropping multiallelic variants. For the specific SNP you mentioned, it was most likely filtered at this step: (you can check this line in your log file. The [x] in your case is probably non-zero. Removed [x] variants that were not SNPs. Note: strand ambiguous SNPs were not dropped. However, if you really want to retain those multi-allelic variants, there is a way to do so. You can try modifying your local version of the MTAG code by turning on one of the (rarely-used) arguments it passes on to ldsc: You can change no_alleles=False to no_alleles=True in line [154] and [156] of mtag.py. I just tested it and this works for me. Multiallelic variants can be retained in the MTAG output this way. For general purpose however, we won't change the current default option (i.e. filtering out multiallelic variants), but maybe we can add an option for the users in the future. Let me know if this tweak works for you, or I can send you the modified copy via email. Best, Hui — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub<#59 (comment)>, or mute the thread<https://github.com/notifications/unsubscribe-auth/Aij-i7C8NvyKAtRJocjyk1sI2sVo04Vsks5u9qsxgaJpZM4ZYmoF>. [https://ipmcdn.avast.com/images/icons/icon-envelope-tick-round-orange-animated-no-repeat-v1.gif]<https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> Virus-free. www.avast.com<https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient>

[huilisabrina]

Hi @julia-ramirez ,

Yes the multi-allelic filter applies to both the effect and alternative allele. (@rkwalters please correct me if this/my understanding of the ldsc munging step is wrong!)

Happy new year!

Best,
Hui

[Nalinikanta7]

Where can I get the Summary statistics? How can I build it in my data? Please explain me in this regard.

[paturley]

Sorry,I don't understand your question. Can you explain what you need in more detail?

…

On Wed, Jan 25, 2023, 7:03 AM NKC_IARI ***@***.***> wrote: Where can I get the Summary statistics? How can I build it in my data? Please explain me in this regard. — Reply to this email directly, view it on GitHub <#59 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFBUB5JILDZVIR4LYGVXPGDWUEJAZANCNFSM4GLCNICQ> . You are receiving this because you commented.Message ID: ***@***.***>

[carbocation]

However, if you really want to retain those multi-allelic variants, there is a way to do so. You can try modifying your local version of the MTAG code by turning on one of the (rarely-used) arguments it passes on to ldsc: You can change no_alleles=False to no_alleles=True in line [154] and [156] of mtag.py. I just tested it and this works for me.

For what it's worth, I created a fork that implemented these changes, but this errors out after encountering an indel:

<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
<>
<> MTAG: Multi-trait Analysis of GWAS 
<> Version: 1.0.8
<> (C) 2017 Omeed Maghzian, Raymond Walters, and Patrick Turley
<> Harvard University Department of Economics / Broad Institute of MIT and Harvard
<> GNU General Public License v3
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
<> Note:  It is recommended to run your own QC on the input before using this program. 
<> Software-related correspondence: jjala.ssgac@gmail.com 
<> All other correspondence: paturley@broadinstitute.org 
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>


Calling ./mtag.py \
--z-name Z \
--se-name SE \
--p-name P \
--bpos-name BP \
--verbose  \
--stream-stdout  \
--n-name NEFF \
--a2-name ALLELE0 \
--n-min 0 \
--a1-name ALLELE1 \
--snp-name SNP \
--chr-name CHR \
--eaf-name A1FREQ \
--sumstats /data/trait1.tsv,/data/trait2.tsv,/data/trait3.tsv,/data/trait4.tsv \
--incld-ambig-snps  \
--beta-name BETA \
--out /data/mtag_out/mtag 

Beginning MTAG analysis...
MTAG will use the Z column for analyses.

[...]

1555812 strand ambiguous SNPs in Trait 1 are included.
1555804 strand ambiguous SNPs in Trait 2 are included.
1555804 strand ambiguous SNPs in Trait 3 are included.
Dropped 505 SNPs due to inconsistent allele pairs from phenotype 4. 10972794 SNPs remain.
Flipped the signs of of 5882129 SNPs to make them consistent with the effect allele orderings of the first trait.
1518125 strand ambiguous SNPs in Trait 4 are included.
... Merge of GWAS summary statistics complete. Number of SNPs:   10972794
Using 9454669 SNPs to estimate Omega (1518125 SNPs excluded due to strand ambiguity)
Estimating sigma..
Preparing phenotype 0 to estimate sigma
Preparing phenotype 1 to estimate sigma
Preparing phenotype 2 to estimate sigma
Preparing phenotype 3 to estimate sigma
created Logger instance to pass through ldsc.
Reading reference panel LD Score from /install/mtag/ld_ref_panel/eur_w_ld_chr/[1-22] ...
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from /install/mtag/ld_ref_panel/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1182588 SNPs remain.
After merging with regression SNP LD, 1182588 SNPs remain.
Computing rg for phenotypes 1/4-1/4
After merging with summary statistics, 1182588 SNPs remain.
1182586 SNPs with valid alleles.
'AACAAC'
Traceback (most recent call last):
  File "/install/mtag/mtag.py", line 1577, in <module>
    mtag(args)
  File "/install/mtag/mtag.py", line 1355, in mtag
    args.sigma_hat = estimate_sigma(DATA[not_SA], args)
  File "/install/mtag/mtag.py", line 472, in estimate_sigma
    rg_results =  sumstats_sig.estimate_rg(args_ldsc_rg, Logger_to_Logging())
  File "/install/mtag/ldsc_mod/ldscore/sumstats.py", line 442, in estimate_rg
    loop = _read_other_sumstats(args, log, None, sumstats, ref_ld_cnames,sumstats2=p2)
  File "/install/mtag/ldsc_mod/ldscore/sumstats.py", line 494, in _read_other_sumstats
    loop['Z2'] = _align_alleles(loop.Z2, alleles)
  File "/install/mtag/ldsc_mod/ldscore/sumstats.py", line 567, in _align_alleles
    z *= (-1) ** alleles.apply(lambda y: FLIP_ALLELES[y])
  File "/usr/local/lib/python2.7/dist-packages/pandas/core/series.py", line 3591, in apply
    mapped = lib.map_infer(values, f, convert=convert_dtype)
  File "pandas/_libs/lib.pyx", line 2217, in pandas._libs.lib.map_infer
  File "/install/mtag/ldsc_mod/ldscore/sumstats.py", line 567, in <lambda>
    z *= (-1) ** alleles.apply(lambda y: FLIP_ALLELES[y])
KeyError: 'AACAAC'

[test12138jooh]

However, if you really want to retain those multi-allelic variants, there is a way to do so. You can try modifying your local version of the MTAG code by turning on one of the (rarely-used) arguments it passes on to ldsc: You can change no_alleles=False to no_alleles=True in line [154] and [156] of mtag.py. I just tested it and this works for me.

For what it's worth, I created a fork that implemented these changes, but this errors out after encountering an indel:

<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
<>
<> MTAG: Multi-trait Analysis of GWAS 
<> Version: 1.0.8
<> (C) 2017 Omeed Maghzian, Raymond Walters, and Patrick Turley
<> Harvard University Department of Economics / Broad Institute of MIT and Harvard
<> GNU General Public License v3
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
<> Note:  It is recommended to run your own QC on the input before using this program. 
<> Software-related correspondence: jjala.ssgac@gmail.com 
<> All other correspondence: paturley@broadinstitute.org 
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>


Calling ./mtag.py \
--z-name Z \
--se-name SE \
--p-name P \
--bpos-name BP \
--verbose  \
--stream-stdout  \
--n-name NEFF \
--a2-name ALLELE0 \
--n-min 0 \
--a1-name ALLELE1 \
--snp-name SNP \
--chr-name CHR \
--eaf-name A1FREQ \
--sumstats /data/trait1.tsv,/data/trait2.tsv,/data/trait3.tsv,/data/trait4.tsv \
--incld-ambig-snps  \
--beta-name BETA \
--out /data/mtag_out/mtag 

Beginning MTAG analysis...
MTAG will use the Z column for analyses.

[...]

1555812 strand ambiguous SNPs in Trait 1 are included.
1555804 strand ambiguous SNPs in Trait 2 are included.
1555804 strand ambiguous SNPs in Trait 3 are included.
Dropped 505 SNPs due to inconsistent allele pairs from phenotype 4. 10972794 SNPs remain.
Flipped the signs of of 5882129 SNPs to make them consistent with the effect allele orderings of the first trait.
1518125 strand ambiguous SNPs in Trait 4 are included.
... Merge of GWAS summary statistics complete. Number of SNPs:   10972794
Using 9454669 SNPs to estimate Omega (1518125 SNPs excluded due to strand ambiguity)
Estimating sigma..
Preparing phenotype 0 to estimate sigma
Preparing phenotype 1 to estimate sigma
Preparing phenotype 2 to estimate sigma
Preparing phenotype 3 to estimate sigma
created Logger instance to pass through ldsc.
Reading reference panel LD Score from /install/mtag/ld_ref_panel/eur_w_ld_chr/[1-22] ...
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from /install/mtag/ld_ref_panel/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1182588 SNPs remain.
After merging with regression SNP LD, 1182588 SNPs remain.
Computing rg for phenotypes 1/4-1/4
After merging with summary statistics, 1182588 SNPs remain.
1182586 SNPs with valid alleles.
'AACAAC'
Traceback (most recent call last):
  File "/install/mtag/mtag.py", line 1577, in <module>
    mtag(args)
  File "/install/mtag/mtag.py", line 1355, in mtag
    args.sigma_hat = estimate_sigma(DATA[not_SA], args)
  File "/install/mtag/mtag.py", line 472, in estimate_sigma
    rg_results =  sumstats_sig.estimate_rg(args_ldsc_rg, Logger_to_Logging())
  File "/install/mtag/ldsc_mod/ldscore/sumstats.py", line 442, in estimate_rg
    loop = _read_other_sumstats(args, log, None, sumstats, ref_ld_cnames,sumstats2=p2)
  File "/install/mtag/ldsc_mod/ldscore/sumstats.py", line 494, in _read_other_sumstats
    loop['Z2'] = _align_alleles(loop.Z2, alleles)
  File "/install/mtag/ldsc_mod/ldscore/sumstats.py", line 567, in _align_alleles
    z *= (-1) ** alleles.apply(lambda y: FLIP_ALLELES[y])
  File "/usr/local/lib/python2.7/dist-packages/pandas/core/series.py", line 3591, in apply
    mapped = lib.map_infer(values, f, convert=convert_dtype)
  File "pandas/_libs/lib.pyx", line 2217, in pandas._libs.lib.map_infer
  File "/install/mtag/ldsc_mod/ldscore/sumstats.py", line 567, in <lambda>
    z *= (-1) ** alleles.apply(lambda y: FLIP_ALLELES[y])
KeyError: 'AACAAC'

I also came across the same problem. Have you solved it?

[paturley]

Sounds like James found a workaround, though this isn't implemented in the current version of MTAG, so you'll have to implement it in your local copy. We unfortunately don't have bandwidth to implement and test this in the official version. Best, Patrick

…

On Mon, Apr 22, 2024, 10:23 PM test12138jooh ***@***.***> wrote: However, if you really want to retain those multi-allelic variants, there is a way to do so. You can try modifying your local version of the MTAG code by turning on one of the (rarely-used) arguments it passes on to ldsc: You can change no_alleles=False to no_alleles=True in line [154] and [156] of mtag.py. I just tested it and this works for me. For what it's worth, I created a fork that implemented these changes <carbocation/mtag_indel@51cbc8f>, but this errors out after encountering an indel: <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> MTAG: Multi-trait Analysis of GWAS <> Version: 1.0.8<> (C) 2017 Omeed Maghzian, Raymond Walters, and Patrick Turley<> Harvard University Department of Economics / Broad Institute of MIT and Harvard<> GNU General Public License v3<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Note: It is recommended to run your own QC on the input before using this program. <> Software-related correspondence: ***@***.*** <> All other correspondence: ***@***.*** <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>Calling ./mtag.py \--z-name Z \--se-name SE \--p-name P \--bpos-name BP \--verbose \--stream-stdout \--n-name NEFF \--a2-name ALLELE0 \--n-min 0 \--a1-name ALLELE1 \--snp-name SNP \--chr-name CHR \--eaf-name A1FREQ \--sumstats /data/trait1.tsv,/data/trait2.tsv,/data/trait3.tsv,/data/trait4.tsv \--incld-ambig-snps \--beta-name BETA \--out /data/mtag_out/mtag Beginning MTAG analysis...MTAG will use the Z column for analyses.[...]1555812 strand ambiguous SNPs in Trait 1 are included.1555804 strand ambiguous SNPs in Trait 2 are included.1555804 strand ambiguous SNPs in Trait 3 are included.Dropped 505 SNPs due to inconsistent allele pairs from phenotype 4. 10972794 SNPs remain.Flipped the signs of of 5882129 SNPs to make them consistent with the effect allele orderings of the first trait.1518125 strand ambiguous SNPs in Trait 4 are included.... Merge of GWAS summary statistics complete. Number of SNPs: 10972794Using 9454669 SNPs to estimate Omega (1518125 SNPs excluded due to strand ambiguity)Estimating sigma..Preparing phenotype 0 to estimate sigmaPreparing phenotype 1 to estimate sigmaPreparing phenotype 2 to estimate sigmaPreparing phenotype 3 to estimate sigmacreated Logger instance to pass through ldsc.Reading reference panel LD Score from /install/mtag/ld_ref_panel/eur_w_ld_chr/[1-22] ...Read reference panel LD Scores for 1290028 SNPs.Removing partitioned LD Scores with zero variance.Reading regression weight LD Score from /install/mtag/ld_ref_panel/eur_w_ld_chr/[1-22] ...Read regression weight LD Scores for 1290028 SNPs.After merging with reference panel LD, 1182588 SNPs remain.After merging with regression SNP LD, 1182588 SNPs remain.Computing rg for phenotypes 1/4-1/4After merging with summary statistics, 1182588 SNPs remain.1182586 SNPs with valid alleles.'AACAAC'Traceback (most recent call last): File "/install/mtag/mtag.py", line 1577, in <module> mtag(args) File "/install/mtag/mtag.py", line 1355, in mtag args.sigma_hat = estimate_sigma(DATA[not_SA], args) File "/install/mtag/mtag.py", line 472, in estimate_sigma rg_results = sumstats_sig.estimate_rg(args_ldsc_rg, Logger_to_Logging()) File "/install/mtag/ldsc_mod/ldscore/sumstats.py", line 442, in estimate_rg loop = _read_other_sumstats(args, log, None, sumstats, ref_ld_cnames,sumstats2=p2) File "/install/mtag/ldsc_mod/ldscore/sumstats.py", line 494, in _read_other_sumstats loop['Z2'] = _align_alleles(loop.Z2, alleles) File "/install/mtag/ldsc_mod/ldscore/sumstats.py", line 567, in _align_alleles z *= (-1) ** alleles.apply(lambda y: FLIP_ALLELES[y]) File "/usr/local/lib/python2.7/dist-packages/pandas/core/series.py", line 3591, in apply mapped = lib.map_infer(values, f, convert=convert_dtype) File "pandas/_libs/lib.pyx", line 2217, in pandas._libs.lib.map_infer File "/install/mtag/ldsc_mod/ldscore/sumstats.py", line 567, in <lambda> z *= (-1) ** alleles.apply(lambda y: FLIP_ALLELES[y])KeyError: 'AACAAC' I also came across the same problem. Have you solved it? — Reply to this email directly, view it on GitHub <#59 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AFBUB5O773EUQ664YJ3ATFDY6XAY5AVCNFSM4GLCNIC2U5DIOJSWCZC7NNSXTN2JONZXKZKDN5WW2ZLOOQ5TEMBXGEZDQOJZGA2Q> . You are receiving this because you commented.Message ID: ***@***.***>