Summary

Summary {data-width=650}

Manhattan plot

manhattan_plot

QQ plot

qq_plot

AF plot

af_plot

P-Z plot

pz_plot

beta_std plot

beta_std_plot

Metadata

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}
 

LDSC

*********************************************************************
* 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/2fec828c-1df9-4954-924b-acd24252146b/call-ldsc/inputs/-261044445/ieu-b-4845.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-4845/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/ 

Beginning analysis at Wed Jan  5 14:29:43 2022
Reading summary statistics from /data/cromwell-executions/qc/2fec828c-1df9-4954-924b-acd24252146b/call-ldsc/inputs/-261044445/ieu-b-4845.vcf.gz ...
Read summary statistics for 7892939 SNPs.
Dropped 38589 SNPs with duplicated rs numbers.
Reading reference panel LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1196616 SNPs remain.
After merging with regression SNP LD, 1196616 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1222 (0.0215)
Lambda GC: 1.0696
Mean Chi^2: 1.0989
Intercept: 1.0283 (0.0099)
Ratio: 0.2864 (0.0997)
Analysis finished at Wed Jan  5 14:31:29 2022
Total time elapsed: 1.0m:45.81s

QC metrics

Metrics

Metrics

{
    "af_correlation": "NA",
    "inflation_factor": 1.062,
    "mean_EFFECT": -0,
    "n": 29212,
    "n_snps": 7892997,
    "n_clumped_hits": 24,
    "n_p_sig": 1892,
    "n_mono": 0,
    "n_ns": 285826,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 7892997,
    "n_miss_AF_reference": 161389,
    "n_est": "NA",
    "ratio_se_n": "NA",
    "mean_diff": "NaN",
    "ratio_diff": "NaN",
    "sd_y_est1": "NaN",
    "sd_y_est2": "NA",
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1196616,
    "ldsc_nsnp_merge_regression_ld": 1196616,
    "ldsc_observed_scale_h2_beta": 0.1222,
    "ldsc_observed_scale_h2_se": 0.0215,
    "ldsc_intercept_beta": 1.0283,
    "ldsc_intercept_se": 0.0099,
    "ldsc_lambda_gc": 1.0696,
    "ldsc_mean_chisq": 1.0989,
    "ldsc_ratio": 0.2861
}
 

Flags

name value
af_correlation NA
inflation_factor FALSE
n FALSE
is_snpid_non_unique TRUE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq FALSE
n_p_sig TRUE
miss_EFFECT FALSE
miss_SE FALSE
miss_PVAL FALSE
ldsc_ratio FALSE
ldsc_intercept_beta FALSE
n_clumped_hits FALSE
r2_sum1 FALSE
r2_sum2 FALSE
r2_sum3 FALSE
r2_sum4 FALSE

Definitions

General metrics

  • af_correlation: Correlation coefficient between AF and AF_reference.
  • inflation_factor (lambda): Genomic inflation factor.
  • mean_EFFECT: Mean of EFFECT size.
  • n: Maximum value of reported sample size across all SNPs, \(n\).
  • n_clumped_hits: Number of clumped hits.
  • n_snps: Number of SNPs
  • n_p_sig: Number of SNPs with pvalue below 5e-8.
  • n_mono: Number of monomorphic (MAF == 1 or MAF == 0) SNPs.
  • n_ns: Number of SNPs with nonsense values:
    • alleles other than A, C, G or T.
    • P-values < 0 or > 1.
    • negative or infinite standard errors (<= 0 or = Infinity).
    • infinite beta estimates or allele frequencies < 0 or > 1.
  • n_mac: Number of cases where MAC (\(2 \times N \times MAF\)) is less than 6.
  • is_snpid_unique: true if the combination of ID REF ALT is unique and therefore no duplication in snpid.
  • n_miss_<*>: Number of NA observations for <*> column.

se_n metrics

  • n_est: Estimated sample size value, \(\widehat{n}\).
  • ratio_se_n: \(\texttt{ratio_se_n} = \frac{\sqrt{\widehat{n}}}{\sqrt{n}}\). We expect ratio_se_n to 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: \(\texttt{mean_diff} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta_j}{\texttt{n_snps}}\), 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.
    • \(\widehat{\beta_j^{std}} = \sqrt{\frac{{z}_j^2 / ({z}_j^2 + n -2)}{2 \times {MAF}_j \times (1 - {MAF}_j)}} \times sign({z}_j)\),
    • \({z}_j = \frac{\beta_j}{{se}_j}\),
    • and \(\beta_j\) is the reported effect size.
  • ratio_diff: \(\texttt{ratio_diff} = |\frac{\texttt{mean_diff}}{\texttt{mean_diff2}}|\), absolute ratio between the mean of diff and the mean of diff2 (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.
    • \(\texttt{mean_diff2} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta^{\prime}_j}{\texttt{n_snps}}\)
    • \(\beta^{\prime}_j = \frac{\beta_j}{\widehat{\texttt{sd2}}_{y}}\)
  • 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.
    • \(\widehat{\texttt{sd1}}_{y} = \frac{\sqrt{n} \times median({se}_j)}{C}\),
    • \(C = median(\frac{1}{\sqrt{2 \times {MAF}_j \times (1 - {MAF}_j)}})\),
    • and \({se}_j\) 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.
    • \(\widehat{\texttt{sd2}}_{y} = median(\widehat{sd_j})\),
    • \(\widehat{sd_j} = \frac{\beta_j}{\widehat{\beta_j^{std}}}\),

r2 metrics

Sum of variance explained, calculated from the clumped top hits sample.

  • r2_sum<*>: r2 statistics under various assumptions
    • 1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).
    • 2: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var2}}}\), \(\texttt{var2} = {\widehat{\texttt{sd1}}_{y}}^2\),
    • 3: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var3}}}\), \(\texttt{var3} = {\widehat{\texttt{sd2}}_{y}}^2\),
    • 4: \(r^2 = \sum_j{\frac{F_j}{F_j + n - 2}}\), \(F = \frac{\beta_j^2}{{se}_j^2}\).

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 \(\chi^2\) statistics.
  • ldsc_ratio: \(\frac{\texttt{ldsc_intercept_beta} - 1}{\texttt{ldsc_mean_chisq} - 1}\), the proportion of the inflation in the mean \(\chi^2\) 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 \(h^2\) 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: NOT is_snpid_unique.
  • mean_EFFECT_nonfinite: mean(EFFECT) is NA, NaN, or Inf.
  • mean_EFFECT_05: abs(mean(EFFECT)) > 0.5.
  • mean_EFFECT_01: abs(mean(EFFECT)) > 0.1.
  • mean_chisq: ldsc_mean_chisq > 1.3 or ldsc_mean_chisq < 0.7.
  • n_p_sig: n_p_sig > 1000.
  • miss_<*>: n_miss_<*> / n_snps > 0.01.
  • ldsc_ratio: ldsc_ratio > 0.5
  • ldsc_intercept_beta: ldsc_intercept_beta > 1.5
  • n_clumped_hits: n_clumped_hits > 1000
  • r2_sum<*>: r2_sum<*> > 0.5

Plots

  • Manhattan plot
    • Red line: \(-log_{10}^{5 \times 10^{-8}}\)
    • Blue line: \(-log_{10}^{5 \times 10^{-5}}\)
  • QQ plot
  • AF plot
  • P-Z plot
  • beta_std plot: Scatter plot between \(\widehat{\beta_j^{std}}\) and \(\beta_j\)

Diagnostics

Details

Summary stats

skim_type skim_variable n_missing complete_rate character.min character.max character.empty character.n_unique character.whitespace logical.mean logical.count numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 14 0.9999982 3 58 0 7892870 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 43 0 16662 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 44 0 6028 0 NA NA NA NA NA NA NA NA NA NA
logical AF 7892997 0.0000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA NA NA 8.643683e+00 5.754032e+00 1.000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.907703e+07 5.635303e+07 3.020e+02 3.280110e+07 6.985278e+07 1.148362e+08 2.492223e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -2.720000e-05 1.747880e-02 -4.784e-01 -8.700000e-03 0.000000e+00 8.700000e-03 2.288000e-01 ▁▁▁▇▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.496970e-02 8.079700e-03 6.100e-03 8.600000e-03 1.140000e-02 1.930000e-02 4.660000e-02 ▇▂▂▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.900493e-01 2.917883e-01 0.000e+00 2.349000e-01 4.870000e-01 7.424992e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.900505e-01 2.917966e-01 0.000e+00 2.348651e-01 4.869789e-01 7.420696e-01 1.000000e+00 ▇▇▇▇▇
numeric AF_reference 161389 0.9795529 NA NA NA NA NA NA NA 2.649049e-01 2.521977e-01 1.997e-04 5.750800e-02 1.793130e-01 4.137380e-01 1.000000e+00 ▇▃▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA NA NA 2.815640e+04 1.056627e+03 2.668e+04 2.668000e+04 2.862700e+04 2.921200e+04 2.921200e+04 ▅▂▁▂▇

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 797440 rs58013264 T C -0.0188 0.0118 0.1119000 0.1111108 NA 0.189497 28103
1 798400 rs10900604 A G -0.0019 0.0098 0.8499999 0.8462718 NA 0.410543 28664
1 798959 rs11240777 G A -0.0022 0.0098 0.8200001 0.8223762 NA 0.409944 28664
1 801467 rs61768212 G C -0.0200 0.0119 0.0908009 0.0928266 NA 0.193091 27833
1 804759 rs7526310 C T -0.0199 0.0119 0.0934997 0.0944713 NA 0.193890 27833
1 808223 rs4951933 G C 0.0052 0.0099 0.5964994 0.5994077 NA 0.452077 26680
1 808631 rs11240779 G A 0.0044 0.0095 0.6392003 0.6432512 NA 0.453474 28418
1 808928 rs11240780 C T 0.0044 0.0095 0.6426996 0.6432512 NA 0.452276 28418
1 845274 rs112856858 G T 0.0116 0.0103 0.2576001 0.2600751 NA 0.374002 26680
1 845635 rs117086422 C T 0.0144 0.0099 0.1488000 0.1457951 NA 0.158546 28418
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51177257 rs73174437 C T -0.0190 0.0228 0.4028998 0.4046568 NA 0.0091853 26680
22 51178607 rs6010067 G C 0.0248 0.0292 0.3952001 0.3957060 NA 0.0814696 26680
22 51180878 rs6010069 A G -0.0059 0.0106 0.5785994 0.5777982 NA 0.1589460 26680
22 51180959 rs6010070 A G -0.0053 0.0106 0.6156998 0.6170751 NA 0.2056710 26680
22 51181685 rs34756634 G A 0.0050 0.0119 0.6726002 0.6743627 NA 0.7118610 26680
22 51181687 rs9616955 A T -0.0159 0.0171 0.3537000 0.3524619 NA 0.0485224 26680
22 51181759 rs13056621 A G -0.0021 0.0121 0.8649000 0.8622162 NA 0.6761180 28418
22 51182090 rs28516879 G A -0.0163 0.0158 0.3012000 0.3022382 NA 0.0467252 27475
22 51185848 rs3865764 G A -0.0075 0.0209 0.7190995 0.7197061 NA 0.9776360 26680
22 51188319 rs139620215 T C -0.0186 0.0359 0.6033996 0.6043844 NA 0.0037939 26680

bcf preview

1   797440  rs58013264  T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0188:0.0118:0.95117:28103:rs58013264
1   798400  rs10900604  A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0019:0.0098:0.0705811:28664:rs10900604
1   798959  rs11240777  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0022:0.0098:0.0861861:28664:rs11240777
1   801467  rs61768212  G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.02:0.0119:1.04191:27833:rs61768212
1   804759  rs7526310   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0199:0.0119:1.02919:27833:rs7526310
1   808223  rs1557576983    G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0052:0.0099:0.22439:26680:rs1557576983
1   808631  rs11240779  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0044:0.0095:0.194363:28418:rs11240779
1   808928  rs1247187939    C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0044:0.0095:0.191992:28418:rs1247187939
1   845274  rs1324283754    G   T   .   PASS    .   ES:SE:LP:SS:ID  0.0116:0.0103:0.589054:26680:rs1324283754
1   845635  rs117086422 C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0144:0.0099:0.827397:28418:rs117086422
1   845938  rs57760052  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.014:0.0099:0.805208:28418:rs57760052
1   846078  rs778265812 C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0122:0.0103:0.629672:28418:rs778265812
1   846398  rs58781670  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0158:0.01:0.945004:28418:rs58781670
1   846465  rs60454217  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0246:0.0306:0.373865:26680:rs60454217
1   846543  rs79396034  G   T   .   PASS    .   ES:SE:LP:SS:ID  0.0308:0.0315:0.484524:26680:rs79396034
1   846808  rs4475691   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0118:0.0102:0.608536:28418:rs4475691
1   846864  rs1269142199    G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0115:0.0102:0.586197:28418:rs1269142199
1   847228  rs3905286   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0131:0.0101:0.71108:28418:rs3905286
1   847491  rs1158719307    G   A   .   PASS    .   ES:SE:LP:SS:ID  0.013:0.01:0.718512:28418:rs1158719307
1   848090  rs4246505   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.013:0.01:0.709965:28418:rs4246505
1   848445  rs1156895099    G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0139:0.01:0.778847:28418:rs1156895099
1   848456  rs11507767  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0141:0.01:0.79915:28418:rs11507767
1   848738  rs3829741   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0128:0.01:0.699405:28418:rs3829741
1   850062  rs28723578  A   T   .   PASS    .   ES:SE:LP:SS:ID  0.0133:0.01:0.736601:28418:rs28723578
1   850123  rs28622257  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0128:0.01:0.701584:28418:rs28622257
1   851190  rs28609852  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0133:0.01:0.742561:28418:rs28609852
1   851204  rs28552953  G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0113:0.0099:0.596536:28418:rs28552953
1   852664  rs28605311  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0132:0.0101:0.718058:28418:rs28605311
1   852758  rs4970462   G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0132:0.0101:0.717831:28418:rs4970462
1   853239  rs4970380   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0132:0.0101:0.718058:28418:rs4970380
1   853805  rs3748591   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0243:0.0276:0.421475:26680:rs3748591
1   854250  rs7537756   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0142:0.0101:0.79833:28418:rs7537756
1   854429  rs72902552  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.026:0.0275:0.462685:26680:rs72902552
1   856436  rs34105146  TG  T   .   PASS    .   ES:SE:LP:SS:ID  0.0096:0.0106:0.440213:26680:rs34105146
1   856476  rs4040605   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0018:0.0084:0.0798767:28418:rs4040605
1   857177  rs386627408 T   C   .   PASS    .   ES:SE:LP:SS:ID  0.0218:0.0272:0.372634:26680:rs386627408
1   858040  rs4970460   C   A   .   PASS    .   ES:SE:LP:SS:ID  0.0131:0.0099:0.736127:28418:rs4970460
1   858051  rs4970459   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0113:0.0099:0.598255:28418:rs4970459
1   858801  rs7418179   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0113:0.0095:0.631899:28418:rs7418179
1   859404  rs71509444  C   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0213:0.0275:0.357239:26680:rs71509444
1   859685  rs111572704 G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0073:0.0103:0.318578:26680:rs111572704
1   859690  rs71509445  C   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0261:0.0295:0.424697:26680:rs71509445
1   859701  rs71509446  C   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0261:0.0295:0.424697:26680:rs71509446
1   859913  rs1187056171    A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0298:0.0296:0.504594:26680:rs1187056171
1   860416  rs61464428  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0113:0.0095:0.633204:28418:rs61464428
1   860461  rs57465118  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.02:0.0267:0.344381:26680:rs57465118
1   860521  rs57924093  C   A   .   PASS    .   ES:SE:LP:SS:ID  -0.02:0.0267:0.344381:26680:rs57924093
1   860688  rs60837925  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0117:0.0095:0.657577:28418:rs60837925
1   860854  rs57816555  T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0272:0.0313:0.41375:26680:rs57816555
1   861008  rs28521172  G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.02:0.0267:0.344381:26680:rs28521172