manhattan_plot
qq_plot
af_plot
pz_plot
beta_std_plot
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"Gwas2VCF_command": "--data /data/cromwell-executions/qc/a11061ae-78f0-4fcd-99aa-578156004707/call-vcf/inputs/562856221/upload.txt.gz --id ieu-b-43 --json /data/cromwell-executions/qc/a11061ae-78f0-4fcd-99aa-578156004707/call-vcf/inputs/562856221/ieu-b-43_data.json --ref /data/cromwell-executions/qc/a11061ae-78f0-4fcd-99aa-578156004707/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/a11061ae-78f0-4fcd-99aa-578156004707/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/ieu-b-43/ieu-b-43.vcf.gz --rm_chr_prefix --cohort_cases 515 --cohort_controls 2509; 1.2.1",
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}
*********************************************************************
* LD Score Regression (LDSC)
* Version 1.0.1
* (C) 2014-2019 Brendan Bulik-Sullivan and Hilary Finucane
* Broad Institute of MIT and Harvard / MIT Department of Mathematics
* GNU General Public License v3
*********************************************************************
Call:
./ldsc.py \
--h2 /data/cromwell-executions/qc/a11061ae-78f0-4fcd-99aa-578156004707/call-ldsc/inputs/562856221/ieu-b-43.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-43/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Fri Aug 21 17:39:07 2020
Reading summary statistics from /data/cromwell-executions/qc/a11061ae-78f0-4fcd-99aa-578156004707/call-ldsc/inputs/562856221/ieu-b-43.vcf.gz ...
Traceback (most recent call last):
File "./ldsc/ldsc.py", line 647, in <module>
sumstats.estimate_h2(args, log)
File "/ldsc/ldscore/sumstats.py", line 330, in estimate_h2
args, log, args.h2)
File "/ldsc/ldscore/sumstats.py", line 246, in _read_ld_sumstats
sumstats = _read_sumstats(args, log, fh, alleles=alleles, dropna=dropna)
File "/ldsc/ldscore/sumstats.py", line 167, in _read_sumstats
sumstats = ps.sumstats(fh, alleles=alleles, dropna=dropna)
File "/ldsc/ldscore/parse.py", line 85, in sumstats
x = read_vcf(fh, alleles, slh)
File "/ldsc/ldscore/parse.py", line 137, in read_vcf
o = [[rec.id, rec.samples[sample]['ES'][0]/rec.samples[sample]['SE'][0], rec.samples[sample]['SS'][0]] for rec in vcf_in.fetch()]
ZeroDivisionError: float division by zero
Analysis finished at Fri Aug 21 17:39:07 2020
Total time elapsed: 0.05s{
"af_correlation": 0.913,
"inflation_factor": 1.0581,
"mean_EFFECT": 0.0014,
"n": 3024,
"n_snps": 399503,
"n_clumped_hits": 1,
"n_p_sig": 1,
"n_mono": 0,
"n_ns": 964,
"n_mac": 0,
"is_snpid_unique": true,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 0,
"n_miss_AF_reference": 2283,
"n_est": 3030.2449,
"ratio_se_n": 1.001,
"mean_diff": "NaN",
"ratio_diff": "NaN",
"sd_y_est1": 2.6617,
"sd_y_est2": 2.6645,
"r2_sum1": 0.115,
"r2_sum2": 0.0162,
"r2_sum3": 0.0162,
"r2_sum4": 0.0148,
"ldsc_nsnp_merge_refpanel_ld": "NA",
"ldsc_nsnp_merge_regression_ld": "NA",
"ldsc_observed_scale_h2_beta": "NA",
"ldsc_observed_scale_h2_se": "NA",
"ldsc_intercept_beta": "NA",
"ldsc_intercept_se": "NA",
"ldsc_lambda_gc": "NA",
"ldsc_mean_chisq": "NA",
"ldsc_ratio": "NA"
}
| name | value |
|---|---|
| name | value |
| af_correlation | FALSE |
| inflation_factor | FALSE |
| n | TRUE |
| is_snpid_non_unique | FALSE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | NA |
| n_p_sig | FALSE |
| miss_EFFECT | FALSE |
| miss_SE | FALSE |
| miss_PVAL | FALSE |
| ldsc_ratio | NA |
| ldsc_intercept_beta | NA |
| n_clumped_hits | FALSE |
| r2_sum1 | FALSE |
| r2_sum2 | FALSE |
| r2_sum3 | FALSE |
| r2_sum4 | FALSE |
General metrics
af_correlation: Correlation coefficient betweenAFandAF_reference.inflation_factor(lambda): Genomic inflation factor.mean_EFFECT: Mean ofEFFECTsize.n: Maximum value of reported sample size across all SNPs, n.n_clumped_hits: Number of clumped hits.n_snps: Number of SNPsn_p_sig: Number of SNPs with pvalue below5e-8.n_mono: Number of monomorphic (MAF == 1orMAF == 0) SNPs.n_ns: Number of SNPs with nonsense values:- alleles other than
A, C, G or T. - P-values
< 0or> 1. - negative or infinite standard errors (
<= 0or= Infinity). - infinite beta estimates or allele frequencies
< 0or> 1.
- alleles other than
n_mac: Number of cases whereMAC(2×N×MAF) is less than6.is_snpid_unique:trueif the combination ofIDREFALTis unique and therefore no duplication in snpid.n_miss_<*>: Number ofNAobservations for<*>column.
se_n metrics
n_est: Estimated sample size value, ˆn.ratio_se_n: ratio_se_n=√ˆn√n. We expectratio_se_nto be 1. When it is not 1, it implies that the trait did not have a variance of 1, the reported sample size is wrong, or that the SNP-level effective sample sizes differ markedly from the reported sample size.mean_diff: mean_diff=∑j^βstdj−βjn_snps, mean difference between the standardised beta, predicted from P-values, and the observed beta. The difference should be very close to zero if trait has a variance of 1.- ^βstdj=√z2j/(z2j+n−2)2×MAFj×(1−MAFj)×sign(zj),
- zj=βjsej,
- and βj is the reported effect size.
ratio_diff: ratio_diff=|mean_diffmean_diff2|, absolute ratio between the mean ofdiffand the mean ofdiff2(expected difference between the standardised beta predicted from P-values, and the standardised beta derived from the observed beta divided by the predicted SD; NOT reported). The ratio should be close to 1. If different from 1, then implies that the betas are not in a standard deviation scale.- mean_diff2=∑j^βstdj−β′jn_snps
- β′j=βj^sd2y
sd_y_est1: The standard deviation for the trait inferred from the reported sample size, median standard errors for the SNP-trait assocations and SNP variances.- ^sd1y=√n×median(sej)C,
- C=median(1√2×MAFj×(1−MAFj)),
- and sej is the reported standard error.
sd_y_est2: The standard deviation for the trait inferred from the reported sample size, Z statistics for the SNP-trait effects (beta/se) and allele frequency.- ^sd2y=median(^sdj),
- ^sdj=βj^βstdj,
r2 metrics
Sum of variance explained, calculated from the clumped top hits sample.
r2_sum<*>:r2statistics under various assumptions1: r2=∑j2×β2j×MAFj×(1−MAFj)var1, var1=1.2: r2=∑j2×β2j×MAFj×(1−MAFj)var2, var2=^sd1y2,3: r2=∑j2×β2j×MAFj×(1−MAFj)var3, var3=^sd2y2,4: r2=∑jFjFj+n−2, F=β2jse2j.
LDSC metrics
Metrics from LD regression
ldsc_nsnp_merge_refpanel_ld: Number of remaining SNPs after merging with reference panel LD.ldsc_nsnp_merge_regression_ld: Number of remaining SNPs after merging with regression SNP LD.ldsc_observed_scale_h2_{beta,se}Coefficient value and SE for total observed scale h2.ldsc_intercept_{beta,se}: Coefficient value and SE for intercept. Intercept is expected to be 1.ldsc_lambda_gc: Lambda GC statistics.ldsc_mean_chisq: Mean χ2 statistics.ldsc_ratio: ldsc_intercept_beta−1ldsc_mean_chisq−1, the proportion of the inflation in the mean χ2 that the LD Score regression intercepts ascribes to causes other than polygenic heritability. The value of ratio should be close to zero, though in practice values of 0.1-0.2 are not uncommon, probably due to sample/reference LD Score mismatch or model misspecification (e.g., low LD variants have slightly higher h2 per SNP).
Flags
When a metric needs attention, the flag should return TRUE.
af_correlation:abs(af_correlation)< 0.7.inflation_factor:inflation_factor> 1.2.n:n(max reported sample size) < 10000.is_snpid_non_unique: NOTis_snpid_unique.mean_EFFECT_nonfinite:mean(EFFECT)isNA,NaN, orInf.mean_EFFECT_05:abs(mean(EFFECT))> 0.5.mean_EFFECT_01:abs(mean(EFFECT))> 0.1.mean_chisq:ldsc_mean_chisq> 1.3 orldsc_mean_chisq< 0.7.n_p_sig:n_p_sig> 1000.miss_<*>:n_miss_<*>/n_snps> 0.01.ldsc_ratio:ldsc_ratio> 0.5ldsc_intercept_beta:ldsc_intercept_beta> 1.5n_clumped_hits:n_clumped_hits> 1000r2_sum<*>:r2_sum<*>> 0.5
Plots
- Manhattan plot
- Red line: −log5×10−810
- Blue line: −log5×10−510
- QQ plot
- AF plot
- P-Z plot
- beta_std plot: Scatter plot between ^βstdj and βj
| skim_type | skim_variable | n_missing | complete_rate | character.min | character.max | character.empty | character.n_unique | character.whitespace | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| skim_type | skim_variable | n_missing | complete_rate | character.min | character.max | character.empty | character.n_unique | character.whitespace | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
| character | ID | 0 | 1.0000000 | 3 | 25 | 0 | 399503 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 1 | 0 | 4 | 0 | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | 8.935680e+00 | 5.844948e+00 | 1.00000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.200000e+01 | ▇▆▅▃▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | 7.756593e+07 | 5.666760e+07 | 3.30120e+04 | 3.048904e+07 | 6.804957e+07 | 1.142195e+08 | 2.492107e+08 | ▇▆▃▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | 1.445100e-03 | 1.045390e-01 | -1.64818e+00 | -5.921190e-02 | 1.801600e-03 | 6.208820e-02 | 1.095270e+00 | ▁▁▇▇▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | 9.442260e-02 | 3.654460e-02 | 0.00000e+00 | 7.132100e-02 | 8.119480e-02 | 1.040560e-01 | 5.483830e-01 | ▇▂▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | 4.912069e-01 | 2.908254e-01 | 0.00000e+00 | 2.378001e-01 | 4.878002e-01 | 7.422001e-01 | 1.000000e+00 | ▇▇▇▇▇ |
| numeric | PVAL_ztest | 964 | 0.9975870 | NA | NA | NA | NA | NA | 4.899817e-01 | 2.901067e-01 | 0.00000e+00 | 2.372260e-01 | 4.866606e-01 | 7.404859e-01 | 9.996910e-01 | ▇▇▇▇▇ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.592723e-01 | 2.545821e-01 | 1.00057e-02 | 1.423720e-01 | 2.981750e-01 | 5.405390e-01 | 9.899170e-01 | ▇▆▃▃▂ |
| numeric | AF_reference | 2283 | 0.9942854 | NA | NA | NA | NA | NA | 3.611443e-01 | 2.407581e-01 | 1.99700e-04 | 1.615420e-01 | 3.043130e-01 | 5.307510e-01 | 9.974040e-01 | ▇▇▅▃▂ |
| numeric | N | 0 | 1.0000000 | NA | NA | NA | NA | NA | 3.024000e+03 | 0.000000e+00 | 3.02400e+03 | 3.024000e+03 | 3.024000e+03 | 3.024000e+03 | 3.024000e+03 | ▁▁▇▁▁ |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
| 1 | 768448 | rs12562034 | G | A | 0.0256677 | 0.1101710 | 0.8158000 | 0.8157765 | 0.105726 | 0.191893 | 3024 |
| 1 | 1005806 | rs3934834 | C | T | 0.0459289 | 0.0955402 | 0.6307002 | 0.6307095 | 0.148488 | 0.223442 | 3024 |
| 1 | 1018704 | rs9442372 | A | G | 0.1103730 | 0.0694047 | 0.1118001 | 0.1117714 | 0.565981 | 0.611022 | 3024 |
| 1 | 1030565 | rs6687776 | C | T | 0.1972100 | 0.0899950 | 0.0284302 | 0.0284269 | 0.157866 | 0.306709 | 3024 |
| 1 | 1031540 | rs9651273 | A | G | 0.0573114 | 0.0776181 | 0.4603000 | 0.4602855 | 0.726524 | 0.881989 | 3024 |
| 1 | 1048955 | rs4970405 | A | G | 0.1371500 | 0.1089730 | 0.2082001 | 0.2081862 | 0.102731 | 0.110623 | 3024 |
| 1 | 1049950 | rs12726255 | A | G | 0.1527210 | 0.0951169 | 0.1083999 | 0.1083588 | 0.139536 | 0.289736 | 3024 |
| 1 | 1061166 | rs11807848 | T | C | 0.0487902 | 0.0693473 | 0.4817004 | 0.4817049 | 0.413427 | 0.329673 | 3024 |
| 1 | 1062638 | rs9442373 | C | A | -0.0109399 | 0.0665660 | 0.8695000 | 0.8694582 | 0.546023 | 0.574281 | 3024 |
| 1 | 1064979 | rs2298217 | C | T | 0.0582689 | 0.0946618 | 0.5382004 | 0.5381928 | 0.150994 | 0.164736 | 3024 |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
| 22 | 51123505 | rs9616816 | G | A | 0.0554347 | 0.0814161 | 0.4958999 | 0.4959466 | 0.2263520 | 0.3783950 | 3024 |
| 22 | 51146139 | rs5770992 | A | G | 0.0713900 | 0.1050080 | 0.4966003 | 0.4965976 | 0.1161600 | 0.1136180 | 3024 |
| 22 | 51147015 | rs2040487 | A | G | 0.0401818 | 0.0699475 | 0.5656998 | 0.5656589 | 0.4133520 | 0.4556710 | 3024 |
| 22 | 51150473 | rs5770820 | G | A | 0.0079682 | 0.0833507 | 0.9238001 | 0.9238398 | 0.2318380 | 0.2462060 | 3024 |
| 22 | 51151724 | rs6010061 | C | T | 0.0459289 | 0.0697014 | 0.5099000 | 0.5099356 | 0.3985900 | 0.6098240 | 3024 |
| 22 | 51163138 | rs715586 | C | T | 0.0198026 | 0.0929076 | 0.8311999 | 0.8312155 | 0.1541430 | 0.0902556 | 3024 |
| 22 | 51165664 | rs8137951 | G | A | -0.0366640 | 0.0753759 | 0.6267004 | 0.6266727 | 0.2946060 | 0.4063500 | 3024 |
| 22 | 51171693 | rs756638 | G | A | -0.0314907 | 0.0769210 | 0.6822994 | 0.6822534 | 0.2760440 | 0.3049120 | 3024 |
| 22 | 51175626 | rs3810648 | A | G | -0.0441609 | 0.1502820 | 0.7689002 | 0.7688698 | 0.0564032 | 0.1084270 | 3024 |
| 22 | 51178090 | rs2285395 | G | A | -0.0350056 | 0.1620390 | 0.8289999 | 0.8289628 | 0.0479109 | 0.0666933 | 3024 |
1 768448 rs12562034 G A . PASS AF=0.105726 ES:SE:LP:AF:SS:NC:ID 0.0256677:0.110171:0.0884163:0.105726:3024:515:rs12562034
1 1005806 rs3934834 C T . PASS AF=0.148488 ES:SE:LP:AF:SS:NC:ID 0.0459289:0.0955402:0.200177:0.148488:3024:515:rs3934834
1 1018704 rs9442372 A G . PASS AF=0.565981 ES:SE:LP:AF:SS:NC:ID 0.110373:0.0694047:0.951558:0.565981:3024:515:rs9442372
1 1030565 rs6687776 C T . PASS AF=0.157866 ES:SE:LP:AF:SS:NC:ID 0.19721:0.089995:1.54622:0.157866:3024:515:rs6687776
1 1031540 rs776599533 A G . PASS AF=0.726524 ES:SE:LP:AF:SS:NC:ID 0.0573114:0.0776181:0.336959:0.726524:3024:515:rs776599533
1 1048955 rs4970405 A G . PASS AF=0.102731 ES:SE:LP:AF:SS:NC:ID 0.13715:0.108973:0.681519:0.102731:3024:515:rs4970405
1 1049950 rs12726255 A G . PASS AF=0.139536 ES:SE:LP:AF:SS:NC:ID 0.152721:0.0951169:0.964971:0.139536:3024:515:rs12726255
1 1061166 rs11807848 T C . PASS AF=0.413427 ES:SE:LP:AF:SS:NC:ID 0.0487902:0.0693473:0.317223:0.413427:3024:515:rs11807848
1 1062638 rs9442373 C A . PASS AF=0.546023 ES:SE:LP:AF:SS:NC:ID -0.0109399:0.066566:0.0607304:0.546023:3024:515:rs9442373
1 1064979 rs2298217 C T . PASS AF=0.150994 ES:SE:LP:AF:SS:NC:ID 0.0582689:0.0946618:0.269056:0.150994:3024:515:rs2298217
1 1066029 rs12145826 G A . PASS AF=0.0720194 ES:SE:LP:AF:SS:NC:ID -0.130564:0.137398:0.465974:0.0720194:3024:515:rs12145826
1 1087683 rs9442380 T C . PASS AF=0.929896 ES:SE:LP:AF:SS:NC:ID 0.115186:0.138468:0.392009:0.929896:3024:515:rs9442380
1 1090557 rs7553429 A C . PASS AF=0.0272798 ES:SE:LP:AF:SS:NC:ID 0.08158:0.204526:0.161214:0.0272798:3024:515:rs7553429
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1 1099342 rs9660710 A C . PASS AF=0.931713 ES:SE:LP:AF:SS:NC:ID 0.081644:0.138859:0.254535:0.931713:3024:515:rs9660710
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1 1163804 rs7515488 C T . PASS AF=0.149761 ES:SE:LP:AF:SS:NC:ID -0.0486652:0.097059:0.210349:0.149761:3024:515:rs7515488
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1 1176597 rs6675798 T C . PASS AF=0.0951217 ES:SE:LP:AF:SS:NC:ID -0.0871932:0.119826:0.330869:0.0951217:3024:515:rs6675798
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1 1505255 rs6603793 C T . PASS AF=0.320414 ES:SE:LP:AF:SS:NC:ID -0.13102:0.0744987:1.10436:0.320414:3024:515:rs6603793
1 1706136 rs6603811 T C . PASS AF=0.949834 ES:SE:LP:AF:SS:NC:ID -0.0158733:0.160353:0.0356932:0.949834:3024:515:rs6603811
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1 1745726 rs16825336 G A . PASS AF=0.0980834 ES:SE:LP:AF:SS:NC:ID 0.0256677:0.11293:0.0860802:0.0980834:3024:515:rs16825336
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1 1810090 rs7525092 C T . PASS AF=0.267745 ES:SE:LP:AF:SS:NC:ID -0.0774211:0.0782072:0.491874:0.267745:3024:515:rs7525092
1 1873625 rs12758705 G A . PASS AF=0.259702 ES:SE:LP:AF:SS:NC:ID -0.0213258:0.078242:0.10502:0.259702:3024:515:rs12758705
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1 2069681 rs3753242 C T . PASS AF=0.0644843 ES:SE:LP:AF:SS:NC:ID 0.0497421:0.138333:0.14315:0.0644843:3024:515:rs3753242
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