paturley
commented
3 years ago What's actually killing you in this case is that the LD score intercept is greater than the mean chi2 stat. That is a bit surprising to me for a trait like LDL. When you run LD score regression on those summary statistics, do you get a very large LD score intercept?
On Thu, Sep 9, 2021 at 7:28 AM mojolin2 @.***> wrote:
Dear mtaggers,
First of all thank you for putting out such a cool and efficient tool. I was running the latest code on some lipids and blood pressure GWAS data from UK Biobank, GWAS from BOLT-LMM. While the job finished, I noticed 'Sigma matrix used is still non-positive-definite.' in the logs. I read in the issues history that this could be related to too high of correlation between the traits, say LDL and Total Chol, r > .95, so I took out the Total Chol but still got the error, it seems like it was because of GWAS mean chi2 for LDL was 0.99, though in the beginning of the log, the MetaData had the chi2 value for LDL (trait 1) to be 1.73. The sample size is about 300K. Any recommendations?
Best- jake
Beginning MTAG analysis... MTAG will use the Z column for analyses. Read in Trait 1 summary statistics (18116736 SNPs) from LDL2_mtag_in.txt ...
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Munging Trait 1 <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Interpreting column names as follows: snpid: Variant ID (e.g., rs number) n: Sample size a1: a1, interpreted as ref allele for signed sumstat. pval: p-Value a2: a2, interpreted as non-ref allele for signed sumstat. z: Directional summary statistic as specified by --signed-sumstats.
Reading sumstats from provided DataFrame into memory 10000000 SNPs at a time. Read 18116736 SNPs from --sumstats file. Removed 1041 SNPs with missing values. Removed 0 SNPs with INFO <= None. Removed 0 SNPs with MAF <= 0.01. Removed 0 SNPs with SE <0 or NaN values. Removed 0 SNPs with out-of-bounds p-values. Removed 1685198 variants that were not SNPs. Note: strand ambiguous SNPs were not dropped. 16430497 SNPs remain. Removed 18699 SNPs with duplicated rs numbers (16411798 SNPs remain). Removed 0 SNPs with N < 0.0 (16411798 SNPs remain). Median value of SIGNED_SUMSTAT was -0.00642259715909, which seems sensible. Dropping snps with null values
Metadata: Mean chi^2 = 1.728 Lambda GC = 1.254 Max chi^2 = 1408.856 62701 Genome-wide significant SNPs (some may have been removed by filtering).
Conversion finished at Thu Sep 9 10:22:05 2021 Total time elapsed: 4.0m:45.41s
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Munging of Trait 1 complete. SNPs remaining: 16430497
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
Trait 1: Dropped 18699 SNPs for duplicate values in the "snp_name" column Read in Trait 2 summary statistics (18116736 SNPs) from HDL_mtag_in.txt ...
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Munging Trait 2 <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Interpreting column names as follows: snpid: Variant ID (e.g., rs number) n: Sample size a1: a1, interpreted as ref allele for signed sumstat. pval: p-Value a2: a2, interpreted as non-ref allele for signed sumstat. z: Directional summary statistic as specified by --signed-sumstats.
Reading sumstats from provided DataFrame into memory 10000000 SNPs at a time. Read 18116736 SNPs from --sumstats file. Removed 1199 SNPs with missing values. Removed 0 SNPs with INFO <= None. Removed 0 SNPs with MAF <= 0.01. Removed 0 SNPs with SE <0 or NaN values. Removed 0 SNPs with out-of-bounds p-values. Removed 1685184 variants that were not SNPs. Note: strand ambiguous SNPs were not dropped. 16430353 SNPs remain. Removed 18699 SNPs with duplicated rs numbers (16411654 SNPs remain). Removed 0 SNPs with N < 0.0 (16411654 SNPs remain). Median value of SIGNED_SUMSTAT was -0.00403410078196, which seems sensible. Dropping snps with null values
Metadata: Mean chi^2 = 1.786 Lambda GC = 1.311 Max chi^2 = 1408.856 58307 Genome-wide significant SNPs (some may have been removed by filtering).
Conversion finished at Thu Sep 9 10:28:44 2021 Total time elapsed: 4.0m:43.8s
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Munging of Trait 2 complete. SNPs remaining: 16430353
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
Trait 2: Dropped 18699 SNPs for duplicate values in the "snp_name" column Read in Trait 3 summary statistics (18116736 SNPs) from TRIG_mtag_in.txt ...
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Munging Trait 3 <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><
<><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Interpreting column names as follows: snpid: Variant ID (e.g., rs number) n: Sample size a1: a1, interpreted as ref allele for signed sumstat. pval: p-Value a2: a2, interpreted as non-ref allele for signed sumstat. z: Directional summary statistic as specified by --signed-sumstats.
Reading sumstats from provided DataFrame into memory 10000000 SNPs at a time. Read 18116736 SNPs from --sumstats file. Removed 1041 SNPs with missing values. ... Sigma hat: [[ 1.769 -0.134 0.288 0.131] [-0.134 1.41 -0.469 -0.043] [ 0.288 -0.469 1.087 0.123] [ 0.131 -0.043 0.123 1.098]] Mean chi^2 of SNPs used to estimate Omega is low for some SNPsMTAG may not perform well in this situation. Beginning estimation of Omega ... Using GMM estimator of Omega .. Checking for positive definiteness .. matrix is not positive definite, performing adjustment.. Warning: max number of iterations reached in adjustment procedure. Sigma matrix used is still non-positive-definite. Completed estimation of Omega ... Beginning MTAG calculations... ... Completed MTAG calculations. Writing Phenotype 1 to file ... Writing Phenotype 2 to file ... Writing Phenotype 3 to file ... Writing Phenotype 4 to file ... Summary of MTAG results:
Trait # SNPs used N (max) N (mean) GWAS mean chi^2 MTAG mean chi^2 GWAS equiv. (max) N 1 LDL2_mtag_in.txt 13874381 340951 340951 0.999 0.943 14945144 2 HDL_mtag_in.txt 13874381 290000 290000 1.285 1.193 196097 3 TRIG_mtag_in.txt 13874381 290000 290000 1.545 1.362 192295 4 SBP_mtag_in.txt 13874381 290000 290000 1.485 1.489 292886
Estimated Omega: [[-6.758e-09 2.021e-11 4.492e-11 -5.942e-13] [ 2.021e-11 1.387e-06 -4.635e-11 -1.009e-11] [ 4.492e-11 -4.635e-11 2.044e-06 6.511e-12] [-5.942e-13 -1.009e-11 6.511e-12 1.835e-06]]
(Correlation): [[ nan nan nan nan] [ nan 1.000e+00 -2.753e-05 -6.326e-06] [ nan -2.753e-05 1.000e+00 3.361e-06] [ nan -6.326e-06 3.361e-06 1.000e+00]]
Estimated Sigma: [[ 1.769 -0.134 0.288 0.131] [-0.134 1.41 -0.469 -0.043] [ 0.288 -0.469 1.087 0.123] [ 0.131 -0.043 0.123 1.098]]
(Correlation): [[ 1. -0.085 0.208 0.094] [-0.085 1. -0.379 -0.035] [ 0.208 -0.379 1. 0.113] [ 0.094 -0.035 0.113 1. ]]
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mojolin2
Dear mtaggers,
First of all thank you for putting out such a cool and efficient tool. I was running the latest code on some lipids and blood pressure GWAS data from UK Biobank, GWAS from BOLT-LMM. While the job finished, I noticed 'Sigma matrix used is still non-positive-definite.' in the logs. I read in the issues history that this could be related to too high of correlation between the traits, say LDL and Total Chol, r > .95, so I took out the Total Chol but still got the error, it seems like it was because of GWAS mean chi2 for LDL was 0.99, though in the beginning of the log, the MetaData had the chi2 value for LDL (trait 1) to be 1.73. The sample size is about 300K. Any recommendations?
Best- jake
Beginning MTAG analysis... MTAG will use the Z column for analyses. Read in Trait 1 summary statistics (18116736 SNPs) from LDL2_mtag_in.txt ... <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Munging Trait 1 <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><>< <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Interpreting column names as follows: snpid: Variant ID (e.g., rs number) n: Sample size a1: a1, interpreted as ref allele for signed sumstat. pval: p-Value a2: a2, interpreted as non-ref allele for signed sumstat. z: Directional summary statistic as specified by --signed-sumstats.
Reading sumstats from provided DataFrame into memory 10000000 SNPs at a time. Read 18116736 SNPs from --sumstats file. Removed 1041 SNPs with missing values. Removed 0 SNPs with INFO <= None. Removed 0 SNPs with MAF <= 0.01. Removed 0 SNPs with SE <0 or NaN values. Removed 0 SNPs with out-of-bounds p-values. Removed 1685198 variants that were not SNPs. Note: strand ambiguous SNPs were not dropped. 16430497 SNPs remain. Removed 18699 SNPs with duplicated rs numbers (16411798 SNPs remain). Removed 0 SNPs with N < 0.0 (16411798 SNPs remain). Median value of SIGNED_SUMSTAT was -0.00642259715909, which seems sensible. Dropping snps with null values
Metadata: Mean chi^2 = 1.728 Lambda GC = 1.254 Max chi^2 = 1408.856 62701 Genome-wide significant SNPs (some may have been removed by filtering).
Conversion finished at Thu Sep 9 10:22:05 2021 Total time elapsed: 4.0m:45.41s <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Munging of Trait 1 complete. SNPs remaining: 16430497 <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
Trait 1: Dropped 18699 SNPs for duplicate values in the "snp_name" column Read in Trait 2 summary statistics (18116736 SNPs) from HDL_mtag_in.txt ... <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Munging Trait 2 <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><>< <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Interpreting column names as follows: snpid: Variant ID (e.g., rs number) n: Sample size a1: a1, interpreted as ref allele for signed sumstat. pval: p-Value a2: a2, interpreted as non-ref allele for signed sumstat. z: Directional summary statistic as specified by --signed-sumstats.
Reading sumstats from provided DataFrame into memory 10000000 SNPs at a time. Read 18116736 SNPs from --sumstats file. Removed 1199 SNPs with missing values. Removed 0 SNPs with INFO <= None. Removed 0 SNPs with MAF <= 0.01. Removed 0 SNPs with SE <0 or NaN values. Removed 0 SNPs with out-of-bounds p-values. Removed 1685184 variants that were not SNPs. Note: strand ambiguous SNPs were not dropped. 16430353 SNPs remain. Removed 18699 SNPs with duplicated rs numbers (16411654 SNPs remain). Removed 0 SNPs with N < 0.0 (16411654 SNPs remain). Median value of SIGNED_SUMSTAT was -0.00403410078196, which seems sensible. Dropping snps with null values
Metadata: Mean chi^2 = 1.786 Lambda GC = 1.311 Max chi^2 = 1408.856 58307 Genome-wide significant SNPs (some may have been removed by filtering).
Conversion finished at Thu Sep 9 10:28:44 2021 Total time elapsed: 4.0m:43.8s <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Munging of Trait 2 complete. SNPs remaining: 16430353 <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
Trait 2: Dropped 18699 SNPs for duplicate values in the "snp_name" column Read in Trait 3 summary statistics (18116736 SNPs) from TRIG_mtag_in.txt ... <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Munging Trait 3 <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><>< <><><<>><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Interpreting column names as follows: snpid: Variant ID (e.g., rs number) n: Sample size a1: a1, interpreted as ref allele for signed sumstat. pval: p-Value a2: a2, interpreted as non-ref allele for signed sumstat. z: Directional summary statistic as specified by --signed-sumstats.
Reading sumstats from provided DataFrame into memory 10000000 SNPs at a time. Read 18116736 SNPs from --sumstats file. Removed 1041 SNPs with missing values. ... Sigma hat: [[ 1.769 -0.134 0.288 0.131] [-0.134 1.41 -0.469 -0.043] [ 0.288 -0.469 1.087 0.123] [ 0.131 -0.043 0.123 1.098]] Mean chi^2 of SNPs used to estimate Omega is low for some SNPsMTAG may not perform well in this situation. Beginning estimation of Omega ... Using GMM estimator of Omega .. Checking for positive definiteness .. matrix is not positive definite, performing adjustment.. Warning: max number of iterations reached in adjustment procedure. Sigma matrix used is still non-positive-definite. Completed estimation of Omega ... Beginning MTAG calculations... ... Completed MTAG calculations. Writing Phenotype 1 to file ... Writing Phenotype 2 to file ... Writing Phenotype 3 to file ... Writing Phenotype 4 to file ...
Summary of MTAG results:
Trait # SNPs used N (max) N (mean) GWAS mean chi^2 MTAG mean chi^2 GWAS equiv. (max) N 1 LDL2_mtag_in.txt 13874381 340951 340951 0.999 0.943 14945144 2 HDL_mtag_in.txt 13874381 290000 290000 1.285 1.193 196097 3 TRIG_mtag_in.txt 13874381 290000 290000 1.545 1.362 192295 4 SBP_mtag_in.txt 13874381 290000 290000 1.485 1.489 292886
Estimated Omega: [[-6.758e-09 2.021e-11 4.492e-11 -5.942e-13] [ 2.021e-11 1.387e-06 -4.635e-11 -1.009e-11] [ 4.492e-11 -4.635e-11 2.044e-06 6.511e-12] [-5.942e-13 -1.009e-11 6.511e-12 1.835e-06]]
(Correlation): [[ nan nan nan nan] [ nan 1.000e+00 -2.753e-05 -6.326e-06] [ nan -2.753e-05 1.000e+00 3.361e-06] [ nan -6.326e-06 3.361e-06 1.000e+00]]
Estimated Sigma: [[ 1.769 -0.134 0.288 0.131] [-0.134 1.41 -0.469 -0.043] [ 0.288 -0.469 1.087 0.123] [ 0.131 -0.043 0.123 1.098]]
(Correlation): [[ 1. -0.085 0.208 0.094] [-0.085 1. -0.379 -0.035] [ 0.208 -0.379 1. 0.113] [ 0.094 -0.035 0.113 1. ]]