Lapack routine dgesv: system is exactly singular: U[5,5] = 0

yuying li

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Mar 1, 2024, 11:17:55 AMMar 1

to Genomic SEM Users

Hi,
Thanks for your work!
Now I'm trying to running GenomicSEM for Common Factor GWAS, but in step 4: Combine the summary statistics and LDSC output and run the common factor GWAS with snp effect
I got the error:

"Lapack routine dgesv: system is exactly singular: U[5,5] = 0"

Calls: commonfactorGWAS -> %dopar% -> <Anonymous>

Here is my code:

AF_factor <- commonfactorGWAS(covstruc = LDSCoutput, SNPs = AF_MGWAS_sumstats , estimation = "DWLS", cores = 15, toler = 1e-60 , SNPSE = FALSE, parallel = TRUE,GC="standard")

here is the head of LDscoutput

$V

[,1] [,2] [,3] [,4] [,5]

[1,] 2.031495e-04 2.199144e-05 -5.543132e-06 1.345035e-05 1.619604e-05

[2,] 2.199144e-05 6.194667e-05 9.888094e-06 2.138668e-06 1.043033e-05

[3,] -5.543132e-06 9.888094e-06 2.537404e-04 -2.656666e-06 -1.785127e-06

[4,] 1.345035e-05 2.138668e-06 -2.656666e-06 1.996487e-05 4.346950e-06

[5,] 1.619604e-05 1.043033e-05 -1.785127e-06 4.346950e-06 1.558995e-05

[6,] 1.222046e-05 2.238214e-05 2.051274e-05 5.457345e-06 5.443163e-06

[7,] -2.144103e-05 3.450537e-06 5.315150e-05 4.470802e-06 4.603180e-06

[8,] 6.777807e-06 3.250754e-06 2.442156e-06 5.488622e-06 3.181726e-06

[9,] 5.057405e-06 8.183235e-06 5.704989e-06 3.786424e-06 7.641225e-06

[10,] 1.398211e-05 2.353221e-05 1.925616e-04 -9.741097e-06 -8.569595e-06

[11,] 7.281276e-06 6.307137e-07 1.089621e-05 9.320047e-06 6.610598e-07

[12,] -7.893921e-06 3.513355e-06 2.615673e-05 6.529312e-07 9.366111e-07

[13,] 3.366773e-06 9.578546e-07 -1.238012e-06 4.608643e-06 2.202693e-06

[14,] 3.125222e-06 1.306617e-06 4.998588e-07 2.830650e-06 2.877961e-06

[15,] 5.490855e-06 3.225999e-06 2.702072e-06 1.414718e-06 6.787058e-06

[,6] [,7] [,8] [,9] [,10]

[1,] 1.222046e-05 -2.144103e-05 6.777807e-06 5.057405e-06 1.398211e-05

[2,] 2.238214e-05 3.450537e-06 3.250754e-06 8.183235e-06 2.353221e-05

[3,] 2.051274e-05 5.315150e-05 2.442156e-06 5.704989e-06 1.925616e-04

[4,] 5.457345e-06 4.470802e-06 5.488622e-06 3.786424e-06 -9.741097e-06

[5,] 5.443163e-06 4.603180e-06 3.181726e-06 7.641225e-06 -8.569595e-06

[6,] 8.376462e-05 9.735481e-06 7.078647e-06 1.870274e-05 1.926272e-05

[7,] 9.735481e-06 1.681985e-04 4.081684e-07 2.183360e-06 1.067483e-04

[8,] 7.078647e-06 4.081684e-07 1.219203e-05 5.847097e-06 6.888067e-06

[9,] 1.870274e-05 2.183360e-06 5.847097e-06 1.799373e-05 7.548939e-06

[10,] 1.926272e-05 1.067483e-04 6.888067e-06 7.548939e-06 1.976499e-03

[11,] 9.084638e-07 1.870238e-05 5.336604e-06 1.878276e-06 1.063224e-05

[12,] 5.075382e-06 3.036960e-05 -4.578612e-07 2.516906e-06 3.263163e-05

[13,] 4.015436e-06 1.462215e-06 3.490226e-06 2.597928e-06 8.458484e-06

[14,] 3.946939e-06 1.497038e-06 3.456198e-06 3.526601e-06 7.533071e-06

[15,] 1.204805e-05 4.903666e-06 3.745328e-06 1.020440e-05 9.716050e-07

[,11] [,12] [,13] [,14] [,15]

[1,] 7.281276e-06 -7.893921e-06 3.366773e-06 3.125222e-06 5.490855e-06

[2,] 6.307137e-07 3.513355e-06 9.578546e-07 1.306617e-06 3.225999e-06

[3,] 1.089621e-05 2.615673e-05 -1.238012e-06 4.998588e-07 2.702072e-06

[4,] 9.320047e-06 6.529312e-07 4.608643e-06 2.830650e-06 1.414718e-06

[5,] 6.610598e-07 9.366111e-07 2.202693e-06 2.877961e-06 6.787058e-06

[6,] 9.084638e-07 5.075382e-06 4.015436e-06 3.946939e-06 1.204805e-05

[7,] 1.870238e-05 3.036960e-05 1.462215e-06 1.497038e-06 4.903666e-06

[8,] 5.336604e-06 -4.578612e-07 3.490226e-06 3.456198e-06 3.745328e-06

[9,] 1.878276e-06 2.516906e-06 2.597928e-06 3.526601e-06 1.020440e-05

[10,] 1.063224e-05 3.263163e-05 8.458484e-06 7.533071e-06 9.716050e-07

[11,] 4.708489e-05 1.654007e-05 4.655501e-06 2.956237e-06 6.488007e-07

[12,] 1.654007e-05 4.461739e-05 2.343577e-08 -3.819908e-07 1.210018e-06

[13,] 4.655501e-06 2.343577e-08 8.741990e-06 3.314809e-06 2.685256e-06

[14,] 2.956237e-06 -3.819908e-07 3.314809e-06 4.864370e-06 4.376309e-06

[15,] 6.488007e-07 1.210018e-06 2.685256e-06 4.376309e-06 2.338487e-05

$S

HF CARDMYO PERICARD PAROXTAC AF

[1,] 0.10573958 0.05862415 0.04589085 0.03256111 0.04449670

[2,] 0.05862415 0.07481068 0.03640735 0.03375220 0.04592359

[3,] 0.04589085 0.03640735 0.01491200 0.01898525 0.02707625

[4,] 0.03256111 0.03375220 0.01898525 0.03585887 0.03168534

[5,] 0.04449670 0.04592359 0.02707625 0.03168534 0.07513127

$I

[,1] [,2] [,3] [,4] [,5]

[1,] 1.04776847 0.156393797 0.042357034 0.016483349 0.12526071

[2,] 0.15639380 1.030435868 0.041963538 -0.002700575 0.20654119

[3,] 0.04235703 0.041963538 1.004571015 -0.003469138 0.03657651

[4,] 0.01648335 -0.002700575 -0.003469138 1.008450766 0.19945965

[5,] 0.12526071 0.206541195 0.036576509 0.199459646 1.10944217

$N

[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]

[1,] 23209.93 30176.08 10202.76 55203.64 81224.31 35903.94 12689.86 68660.03

[,9] [,10] [,11] [,12] [,13] [,14] [,15]

[1,] 101023.9 4104.781 23215.59 34157.62 124732 187739.4 280205.7

$m

[1] 1173569

And I also looked through previous similarly question, and I check there is no 0 for any b and se column.
I'm wondering is that because we have a sumstats dataset only include 4104 individuals? if it's not, could you help me to fix this problem? Thanks in advance!

agro...@gmail.com

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Mar 4, 2024, 8:09:30 AMMar 4

to Genomic SEM Users

I would suggest checking three things:

1. Do you get this error when running a model without SNPs?

2. If you run the code on a subset of 10 SNPs does the error still pop up? My question here is whether this error holds for all of your SNPs or is specific to a handful of SNPs that are causing issues.

3. 4104 is definitely on the smaller end and under powered GWAS tend to cause downstream issues such as this. If your other traits are much larger you could certainly try removing this one and see if the problem persists. The key parameter with respect to power is the SNP-based heritability, which is to say that 4104 could be just enough if you have a highly heritable and well phenotyped trait, but is likely far too small otherwise. The recommendation from the original LDSC developers is to use traits with a SNP-based heritability Z-statistic > 4 in order to produce interpretable estimates of genetic covariance.

yuying li

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Mar 30, 2024, 3:32:55 PMMar 30

to Genomic SEM Users

Hi

Thanks for your suggetions!

I rerun the model without SNPs, at first I tried commonfactor model, but it shows:

[1] "The common factor initially failed to converge. A lower bound of 0 on residual variances has been added to try and troubleshoot this."

[1] "The common factor model failed to converge on a solution. Please try specifying an alternative model using the usermodel function."

Then I tried 2-factor and 3- factor models

Call:

factanal(factors = 2, covmat = Ssmooth, rotation = "promax")

Uniquenesses:

AF_GRCh37 Cardiomyopathy

0.442 0.005

Heart_valve_diseases_(aortic_stenosis) HeartFailure

0.850 0.251

Paroxysmal_tachycardia Pericarditis

0.308 0.005

Loadings:

Factor1 Factor2

AF_GRCh37 0.134 0.664

Cardiomyopathy 1.011

Heart_valve_diseases_(aortic_stenosis) -0.240 0.466

HeartFailure 0.908

Paroxysmal_tachycardia 0.338 0.595

Pericarditis 1.004

Factor1 Factor2

SS loadings 2.227 1.838

Proportion Var 0.371 0.306

Cumulative Var 0.371 0.677

Factor Correlations:

Factor1 Factor2

Factor1 1.000 0.555

Factor2 0.555 1.000

And the corresponding CFA model is like this:

CFAofEFA2f <- 'F1 =~ Cardiomyopathy + Pericarditis + Paroxysmal_tachycardia
             F2 =~ AF_GRCh37 + Heart_valve_diseases_(aortic_stenosis) + HeartFailure + Paroxysmal_tachycardia
            F1~~F2'
Anthro<-usermodel(anthro, estimation = "DWLS", model = CFAofEFA2f, CFIcalc = TRUE, std.lv = TRUE, imp_cov = FALSE, parallel = FALSE)

Call:

factanal(factors = 3, covmat = Ssmooth, rotation = "promax")

Uniquenesses:

AF_GRCh37 Cardiomyopathy

0.422 0.005

Heart_valve_diseases_(aortic_stenosis) HeartFailure

0.005 0.308

Paroxysmal_tachycardia Pericarditis

0.264 0.005

Loadings:

Factor1 Factor2 Factor3

AF_GRCh37 0.750

Cardiomyopathy 0.992

Heart_valve_diseases_(aortic_stenosis) 0.994

HeartFailure -0.135 0.877

Paroxysmal_tachycardia 0.198 0.737

Pericarditis 0.981

Factor1 Factor2 Factor3

SS loadings 2.005 1.875 1.004

Proportion Var 0.334 0.313 0.167

Cumulative Var 0.334 0.647 0.814

Factor Correlations:

Factor1 Factor2 Factor3

Factor1 1.0000 -0.0415 0.610

Factor2 -0.0415 1.0000 0.269

Factor3 0.6099 0.2686 1.000

CFAofEFA3f <- 'F1 =~ Cardiomyopathy + Pericarditis
             F2 =~ AF_GRCh37 + HeartFailure + Paroxysmal_tachycardia
             F3 =~ Heart_valve_diseases_(aortic_stenosis)
            F1~~F2
            F2~~F3
            F1~~F3'
Anthro<-usermodel(anthro, estimation = "DWLS", model = CFAofEFA3f, CFIcalc = TRUE, std.lv = TRUE, imp_cov = FALSE)

But it shows error:

Error in eigen(V_LD) : 0 x 0 matrix

I didn't find any answer about this error, thanks for your time !

yuying li

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Mar 30, 2024, 3:41:57 PMMar 30

to Genomic SEM Users

I forgot to add new LDSCoutput, here it is:

LDSCoutput

$V

[,1] [,2] [,3] [,4] [,5]

[1,] 6.634380e-04 5.922035e-05 6.105079e-05 2.452001e-05 1.219305e-04

[2,] 5.922035e-05 6.192872e-05 -1.713810e-05 4.796429e-06 2.585079e-05

[3,] 6.105079e-05 -1.713810e-05 9.566928e-04 4.561080e-06 6.410901e-05

[4,] 2.452001e-05 4.796429e-06 4.561080e-06 5.219829e-05 4.651135e-06

[5,] 1.219305e-04 2.585079e-05 6.410901e-05 4.651135e-06 8.368324e-05

[6,] 2.026113e-04 2.190326e-04 -6.466438e-05 1.702795e-05 8.990660e-05

[7,] 1.124142e-05 2.148922e-05 -2.966638e-05 4.472015e-06 1.356114e-05

[8,] 2.229469e-05 -8.367692e-06 3.494793e-04 7.467856e-06 1.360570e-05

[9,] -2.804225e-06 5.919124e-06 1.890440e-05 8.293495e-06 1.424664e-06

[10,] 1.293769e-06 1.284126e-05 -1.066283e-05 -3.923771e-06 1.255282e-05

[11,] 2.696624e-05 8.506095e-05 -1.143686e-04 1.901643e-05 3.849050e-05

[12,] 3.118345e-04 1.183130e-04 -3.986870e-04 8.661613e-05 5.143988e-05

[13,] -3.111370e-05 -7.582109e-06 7.189813e-05 7.258930e-06 2.720809e-07

[14,] 3.392233e-05 -3.769431e-06 1.708443e-04 -4.221119e-06 1.021996e-06

[15,] 8.148975e-05 -3.334130e-05 1.285410e-03 2.680681e-05 5.117130e-05

[16,] 1.046518e-05 4.156791e-06 1.562696e-05 4.198132e-06 4.263664e-06

[17,] -4.929012e-07 4.162904e-06 -9.542051e-06 6.022992e-06 7.521804e-06

[18,] -1.157290e-05 2.060207e-05 6.765845e-05 2.932552e-05 4.395275e-06

[19,] 5.671485e-05 6.876690e-06 2.432035e-05 -8.332178e-07 3.432684e-05

[20,] 2.728035e-06 4.574546e-05 -3.519962e-05 -1.380948e-05 4.475861e-05

[21,] 1.262746e-04 2.724515e-04 -3.714117e-04 5.139420e-05 1.685144e-04

[,6] [,7] [,8] [,9] [,10]

[1,] 2.026113e-04 1.124142e-05 2.229469e-05 -2.804225e-06 1.293769e-06

[2,] 2.190326e-04 2.148922e-05 -8.367692e-06 5.919124e-06 1.284126e-05

[3,] -6.466438e-05 -2.966638e-05 3.494793e-04 1.890440e-05 -1.066283e-05

[4,] 1.702795e-05 4.472015e-06 7.467856e-06 8.293495e-06 -3.923771e-06

[5,] 8.990660e-05 1.356114e-05 1.360570e-05 1.424664e-06 1.255282e-05

[6,] 7.768122e-04 7.642568e-05 -3.408236e-05 2.080900e-05 4.637237e-05

[7,] 7.642568e-05 8.776124e-05 -3.976552e-05 4.198629e-06 6.547519e-06

[8,] -3.408236e-05 -3.976552e-05 6.179345e-04 1.094728e-05 1.130748e-06

[9,] 2.080900e-05 4.198629e-06 1.094728e-05 2.120082e-05 -9.727880e-08

[10,] 4.637237e-05 6.547519e-06 1.130748e-06 -9.727880e-08 3.372584e-05

[11,] 3.017917e-04 2.783681e-04 -9.721988e-05 1.588716e-05 3.541168e-05

[12,] 4.377122e-04 9.250781e-05 -1.042353e-03 -1.758833e-05 -8.279628e-05

[13,] -2.461815e-05 4.899213e-06 -9.594649e-06 7.205405e-06 1.545697e-06

[14,] -1.210695e-05 -1.445649e-05 1.035925e-04 -1.950742e-06 -9.501641e-06

[15,] -1.348041e-04 -1.409700e-04 2.251624e-03 3.864469e-05 5.655547e-06

[16,] 1.491790e-05 2.395895e-06 4.877120e-06 1.444903e-06 1.633178e-06

[17,] 1.491519e-05 5.879769e-06 -5.122866e-06 4.138441e-06 2.107078e-06

[18,] 7.265659e-05 1.516162e-05 3.810204e-05 7.454811e-05 -1.339967e-07

[19,] 2.393177e-05 6.496462e-06 2.678122e-05 6.475219e-07 1.655893e-05

[20,] 1.656262e-04 2.262409e-05 6.540637e-06 -2.883009e-07 1.194850e-04

[21,] 9.752449e-04 1.099707e-03 -5.263886e-04 4.887686e-05 8.464518e-05

[,11] [,12] [,13] [,14] [,15]

[1,] 2.696624e-05 3.118345e-04 -3.111370e-05 3.392233e-05 8.148975e-05

[2,] 8.506095e-05 1.183130e-04 -7.582109e-06 -3.769431e-06 -3.334130e-05

[3,] -1.143686e-04 -3.986870e-04 7.189813e-05 1.708443e-04 1.285410e-03

[4,] 1.901643e-05 8.661613e-05 7.258930e-06 -4.221119e-06 2.680681e-05

[5,] 3.849050e-05 5.143988e-05 2.720809e-07 1.021996e-06 5.117130e-05

[6,] 3.017917e-04 4.377122e-04 -2.461815e-05 -1.210695e-05 -1.348041e-04

[7,] 2.783681e-04 9.250781e-05 4.899213e-06 -1.445649e-05 -1.409700e-04

[8,] -9.721988e-05 -1.042353e-03 -9.594649e-06 1.035925e-04 2.251624e-03

[9,] 1.588716e-05 -1.758833e-05 7.205405e-06 -1.950742e-06 3.864469e-05

[10,] 3.541168e-05 -8.279628e-05 1.545697e-06 -9.501641e-06 5.655547e-06

[11,] 1.203079e-03 2.397177e-04 2.250930e-05 -4.862213e-05 -3.444602e-04

[12,] 2.397177e-04 1.573092e-02 3.998182e-04 1.646396e-05 -3.911817e-03

[13,] 2.250930e-05 3.998182e-04 1.959533e-04 3.012789e-05 -3.901054e-05

[14,] -4.862213e-05 1.646396e-05 3.012789e-05 3.349500e-04 3.731003e-04

[15,] -3.444602e-04 -3.911817e-03 -3.901054e-05 3.731003e-04 8.215347e-03

[16,] 1.180938e-05 3.600969e-05 5.159459e-06 3.952247e-06 1.847447e-05

[17,] 1.228824e-05 5.307077e-06 5.834441e-06 -9.994379e-07 -1.879787e-05

[18,] 5.741482e-05 -6.236128e-05 2.519566e-05 -6.241884e-06 1.347104e-04

[19,] 2.437687e-05 3.616198e-05 -3.065632e-07 -1.065065e-05 9.997289e-05

[20,] 1.229302e-04 -2.993341e-04 4.703682e-06 -3.360864e-05 2.923468e-05

[21,] 3.496527e-03 1.331281e-03 7.259531e-05 -1.618936e-04 -1.873581e-03

[,16] [,17] [,18] [,19] [,20]

[1,] 1.046518e-05 -4.929012e-07 -1.157290e-05 5.671485e-05 2.728035e-06

[2,] 4.156791e-06 4.162904e-06 2.060207e-05 6.876690e-06 4.574546e-05

[3,] 1.562696e-05 -9.542051e-06 6.765845e-05 2.432035e-05 -3.519962e-05

[4,] 4.198132e-06 6.022992e-06 2.932552e-05 -8.332178e-07 -1.380948e-05

[5,] 4.263664e-06 7.521804e-06 4.395275e-06 3.432684e-05 4.475861e-05

[6,] 1.491790e-05 1.491519e-05 7.265659e-05 2.393177e-05 1.656262e-04

[7,] 2.395895e-06 5.879769e-06 1.516162e-05 6.496462e-06 2.262409e-05

[8,] 4.877120e-06 -5.122866e-06 3.810204e-05 2.678122e-05 6.540637e-06

[9,] 1.444903e-06 4.138441e-06 7.454811e-05 6.475219e-07 -2.883009e-07

[10,] 1.633178e-06 2.107078e-06 -1.339967e-07 1.655893e-05 1.194850e-04

[11,] 1.180938e-05 1.228824e-05 5.741482e-05 2.437687e-05 1.229302e-04

[12,] 3.600969e-05 5.307077e-06 -6.236128e-05 3.616198e-05 -2.993341e-04

[13,] 5.159459e-06 5.834441e-06 2.519566e-05 -3.065632e-07 4.703682e-06

[14,] 3.952247e-06 -9.994379e-07 -6.241884e-06 -1.065065e-05 -3.360864e-05

[15,] 1.847447e-05 -1.879787e-05 1.347104e-04 9.997289e-05 2.923468e-05

[16,] 8.809876e-06 1.230295e-06 5.411681e-06 4.567398e-06 5.781078e-06

[17,] 1.230295e-06 1.696943e-05 1.451396e-05 -3.088723e-06 7.591733e-06

[18,] 5.411681e-06 1.451396e-05 2.624528e-04 2.492117e-06 -1.968992e-07

[19,] 4.567398e-06 -3.088723e-06 2.492117e-06 6.577462e-05 5.869771e-05

[20,] 5.781078e-06 7.591733e-06 -1.968992e-07 5.869771e-05 4.237385e-04

[21,] 3.188145e-05 7.488160e-05 1.777220e-04 7.602458e-05 2.939419e-04

[,21]

[1,] 1.262746e-04

[2,] 2.724515e-04

[3,] -3.714117e-04

[4,] 5.139420e-05

[5,] 1.685144e-04

[6,] 9.752449e-04

[7,] 1.099707e-03

[8,] -5.263886e-04

[9,] 4.887686e-05

[10,] 8.464518e-05

[11,] 3.496527e-03

[12,] 1.331281e-03

[13,] 7.259531e-05

[14,] -1.618936e-04

[15,] -1.873581e-03

[16,] 3.188145e-05

[17,] 7.488160e-05

[18,] 1.777220e-04

[19,] 7.602458e-05

[20,] 2.939419e-04

[21,] 1.384528e-02

$S

AF_GRCh37 Cardiomyopathy Heart_valve_diseases_(aortic_stenosis)

[1,] 0.5006282 0.091405496 0.154347289

[2,] 0.0914055 0.067310004 0.004267099

[3,] 0.1543473 0.004267099 1.146664084

[4,] 0.0827238 0.020425839 0.070998694

[5,] 0.1170491 0.044840690 0.037167858

[6,] 0.3268329 0.244734845 0.011726896

HeartFailure Paroxysmal_tachycardia Pericarditis

[1,] 0.08272380 0.11704914 0.32683288

[2,] 0.02042584 0.04484069 0.24473485

[3,] 0.07099869 0.03716786 0.01172690

[4,] 0.03613277 0.03212762 0.07319881

[5,] 0.03212762 0.06690655 0.15889559

[6,] 0.07319881 0.15889559 0.85456863

$I

[,1] [,2] [,3] [,4] [,5] [,6]

[1,] 1.10527062 0.129914322 0.0343249836 0.207421816 0.2055565524 0.129827078

[2,] 0.12991432 1.049058888 0.0021870492 0.032163612 0.1604980339 1.013417009

[3,] 0.03432498 0.002187049 1.0343552317 0.068662865 0.0001504762 0.002425674

[4,] 0.20742182 0.032163612 0.0686628646 1.010830695 -0.0040220051 0.032114943

[5,] 0.20555655 0.160498034 0.0001504762 -0.004022005 1.0281760643 0.160713166

[6,] 0.12982708 1.013417009 0.0024256741 0.032114943 0.1607131660 1.049198112

$N

[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]

[1,] 44008.5 31679.94 14409.16 73188.62 39477.84 14367.18 22904.1 10397.85

[,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]

[1,] 52028.28 28252.99 10281.42 4720.47 24131.34 12955.93 4714.237 123831.4

[,17] [,18] [,19] [,20] [,21]

[1,] 64850.56 23606.46 35561.04 12813.83 4709.425

$m

[1] 1173569

agro...@gmail.com

unread,

Apr 1, 2024, 3:30:50 PMApr 1

to Genomic SEM Users

Ok so the issue stems from the model prior to including SNPs. Three other things come to mind based on what you've sent:

1. I'm not certain this would be a problem, but it could be that naming one of your traits with parentheses is a problem: Heart_valve_diseases_(aortic_stenosis)

I would try renaming that trait in your genetic covariance matrix first to see if that fixes that error.

2. One issue is that you have a genetic covariance (S) matrix where this same trait is listed as having a heritability of 1.14. This leads me to think that, separate from the naming issue, this is a particularly underpowered trait. Power issues will often cause various downstream errors to pop-up related to V. The original LDSC developers recommend only including traits with a SNP-based heritabiltiy Z-statistic > 4

3. A three-factor model is likely pushing it for this number of traits. For example, your three-factor solution is not locally identified in a few places due to two and or one-indicator factors. You would want to specify something like this for it to be identified that include equality constraints on the two-indicator factors and fix the resudal variance of the one-factor indicator to 0.

CFAofEFA3f <- 'F1 =~ a*Cardiomyopathy + a*Pericarditis

             F2 =~ AF_GRCh37 + HeartFailure + Paroxysmal_tachycardia

             F3 =~ Heart_valve_diseases_(aortic_stenosis)

Heart_valve_diseases_(aortic_stenosis)~~0*Heart_valve_diseases_(aortic_stenosis)

            F1~~F2

            F2~~F3

            F1~~F3'

I think more generally a recommendation would be to look more closely at your data before jumping ahead to later steps. You have several apparent issues here (unrealistic heritabilites, models that don't converge prior to running the multivariate GWAS) that would ideally be identified before you start looking at individual SNP effects.