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/72d745a0-c44f-468b-9f71-e386413f793a/call-ldsc/inputs/-261044535/ieu-b-4818.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/ieu-b-4818/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/ 

Beginning analysis at Wed Jan  5 05:39:51 2022
Reading summary statistics from /data/cromwell-executions/qc/72d745a0-c44f-468b-9f71-e386413f793a/call-ldsc/inputs/-261044535/ieu-b-4818.vcf.gz ...
Read summary statistics for 8029585 SNPs.
Dropped 39100 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, 1202658 SNPs remain.
After merging with regression SNP LD, 1202658 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1131 (0.0083)
Lambda GC: 1.327
Mean Chi^2: 1.3718
Intercept: 1.1559 (0.009)
Ratio: 0.4192 (0.0241)
Analysis finished at Wed Jan  5 05:41:38 2022
Total time elapsed: 1.0m:46.65s

QC metrics

Metrics

Metrics

{
    "af_correlation": "NA",
    "inflation_factor": 1.3027,
    "mean_EFFECT": -0.0027,
    "n": 97656,
    "n_snps": 8029645,
    "n_clumped_hits": 21,
    "n_p_sig": 428,
    "n_mono": 0,
    "n_ns": 285806,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 8029645,
    "n_miss_AF_reference": 162500,
    "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": 1202658,
    "ldsc_nsnp_merge_regression_ld": 1202658,
    "ldsc_observed_scale_h2_beta": 0.1131,
    "ldsc_observed_scale_h2_se": 0.0083,
    "ldsc_intercept_beta": 1.1559,
    "ldsc_intercept_se": 0.009,
    "ldsc_lambda_gc": 1.327,
    "ldsc_mean_chisq": 1.3718,
    "ldsc_ratio": 0.4193
}
 

Flags

name value
af_correlation NA
inflation_factor TRUE
n FALSE
is_snpid_non_unique TRUE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq TRUE
n_p_sig FALSE
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 16 0.9999980 3 58 0 8029515 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 6027 0 NA NA NA NA NA NA NA NA NA NA
logical AF 8029645 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 8.642174e+00 5.752157e+00 1.0000e+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 7.886231e+07 5.635232e+07 3.0200e+02 3.246903e+07 6.943490e+07 1.146235e+08 2.492223e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -2.729400e-03 2.021198e-01 -2.2171e+00 -1.012000e-01 -1.500000e-03 9.750000e-02 2.232500e+00 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.566817e-01 8.969840e-02 7.4600e-02 8.840000e-02 1.174000e-01 2.011000e-01 5.434000e-01 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.576632e-01 2.994424e-01 0.0000e+00 1.876001e-01 4.413997e-01 7.169997e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.576634e-01 2.994426e-01 0.0000e+00 1.876387e-01 4.414103e-01 7.170173e-01 1.000000e+00 ▇▆▆▆▆
numeric AF_reference 162500 0.9797625 NA NA NA NA NA NA NA 2.622305e-01 2.522768e-01 1.9970e-04 5.471250e-02 1.757190e-01 4.097440e-01 1.000000e+00 ▇▃▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA NA NA 9.112486e+04 9.872957e+03 5.8712e+04 9.170500e+04 9.463700e+04 9.725800e+04 9.765600e+04 ▁▁▁▁▇

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 768448 rs12562034 G A 0.0542 0.1680 0.7471005 0.7469838 NA 0.1918930 60554
1 777232 rs112618790 C T 0.0256 0.1788 0.8863001 0.8861506 NA 0.0668930 60554
1 791853 rs6684487 G A -0.0627 0.1776 0.7240007 0.7240580 NA 0.0762780 60554
1 794332 rs12127425 G A -0.0618 0.1962 0.7528995 0.7527733 NA 0.1162140 58712
1 795222 rs12131377 C G -0.0455 0.1930 0.8137000 0.8136257 NA 0.0696885 59452
1 796100 rs12132398 C T -0.0466 0.1932 0.8092000 0.8093995 NA 0.0856629 59452
1 797281 rs76631953 G C -0.0489 0.1940 0.8008001 0.8009933 NA 0.0656949 59452
1 797325 rs111739932 T C -0.0436 0.1921 0.8206000 0.8204509 NA 0.0680911 59452
1 797440 rs58013264 T C 0.0099 0.1371 0.9425001 0.9424347 NA 0.1894970 81447
1 798026 rs4951864 C T 0.0324 0.1886 0.8634999 0.8636009 NA 0.8941690 60095
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51195550 rs148968329 C A 0.0573 0.2085 0.7835000 0.7834544 NA 0.0391374 59452
22 51196164 rs8136603 A T -0.1164 0.2205 0.5975001 0.5975749 NA 0.1427720 58712
22 51196296 rs9616961 G C 0.0467 0.2087 0.8229999 0.8229393 NA 0.0395367 59452
22 51197576 rs147713773 G C 0.0460 0.2095 0.8262000 0.8262057 NA 0.0471246 59452
22 51197602 rs187225588 T A -0.0825 0.2236 0.7123001 0.7121557 NA 0.0175719 58712
22 51198569 rs142671391 G C -0.2514 0.2569 0.3278000 0.3277822 NA 0.1110220 58712
22 51198906 rs6010079 G A 0.0352 0.2098 0.8668000 0.8667573 NA 0.0421326 59452
22 51202748 rs9616963 A G 0.0102 0.2101 0.9612999 0.9612793 NA 0.0391374 59452
22 51208568 rs148425445 G T -0.2203 0.2233 0.3239000 0.3238559 NA 0.1160140 58712
22 51222100 rs114553188 G T -0.3142 0.2624 0.2313002 0.2311474 NA 0.0880591 58712

bcf preview

1   768448  rs12562034  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0542:0.168:0.126621:60554:rs12562034
1   777232  rs112618790 C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0256:0.1788:0.0524192:60554:rs112618790
1   791853  rs6684487   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0627:0.1776:0.140261:60554:rs6684487
1   794332  rs12127425  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0618:0.1962:0.123263:58712:rs12127425
1   795222  rs12131377  C   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0455:0.193:0.0895357:59452:rs12131377
1   796100  rs12132398  C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0466:0.1932:0.0919441:59452:rs12132398
1   797281  rs1347695410    G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0489:0.194:0.0964759:59452:rs1347695410
1   797325  rs1338750774    T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0436:0.1921:0.0858685:59452:rs1338750774
1   797440  rs58013264  T   C   .   PASS    .   ES:SE:LP:SS:ID  0.0099:0.1371:0.0257186:81447:rs58013264
1   798026  rs4951864   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0324:0.1886:0.0637377:60095:rs4951864
1   798400  rs10900604  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0618:0.1124:0.234779:83094:rs10900604
1   798801  rs12132517  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0459:0.1916:0.0910863:59452:rs12132517
1   798959  rs11240777  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0768:0.1123:0.305922:83094:rs11240777
1   799499  rs147634896 T   C   .   PASS    .   ES:SE:LP:SS:ID  0.0001:0.1925:0.000130308:59452:rs147634896
1   800383  rs4951931   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0355:0.1883:0.0703257:59911:rs4951931
1   801467  rs61768212  G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0036:0.1378:0.00908425:80804:rs61768212
1   801661  rs12132974  C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0563:0.1978:0.110138:58712:rs12132974
1   801680  rs12134490  A   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0594:0.1979:0.116907:58712:rs12134490
1   801858  rs17276806  C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0504:0.198:0.0973445:58712:rs17276806
1   801943  rs7516866   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.081:0.1904:0.173666:59911:rs7516866
1   802856  rs139867617 C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0288:0.1987:0.0532531:58712:rs139867617
1   804759  rs7526310   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0232:0.1379:0.0622816:80804:rs7526310
1   808223  rs1557576983    G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0375:0.1226:0.119301:65615:rs1557576983
1   808631  rs11240779  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0035:0.1085:0.0113965:82187:rs11240779
1   808928  rs1247187939    C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0002:0.1085:0.00056495:82187:rs1247187939
1   833223  rs13303211  C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0886:0.1223:0.32892:60095:rs13303211
1   833302  rs28752186  C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0907:0.1223:0.33885:60095:rs28752186
1   833641  rs28594623  T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0807:0.122:0.293794:60095:rs28594623
1   833824  rs28484835  T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0913:0.1222:0.342084:60095:rs28484835
1   833927  rs28593608  T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.1289:0.1245:0.522156:60095:rs28593608
1   834056  rs28482280  A   C   .   PASS    .   ES:SE:LP:SS:ID  0.3125:0.3545:0.422508:58712:rs28482280
1   834198  rs28385272  T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.1235:0.1245:0.49363:60095:rs28385272
1   834832  rs796468152 G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0907:0.122:0.339609:60095:rs796468152
1   834928  rs4422949   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.1217:0.1244:0.484391:60095:rs4422949
1   834956  rs7518581   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.3387:0.3557:0.467118:58712:rs7518581
1   834999  rs28570054  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.1285:0.1243:0.521289:60095:rs28570054
1   835092  rs72631887  T   G   .   PASS    .   ES:SE:LP:SS:ID  -0.1492:0.3499:0.17399:58712:rs72631887
1   835499  rs4422948   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0281:0.1215:0.0877779:60095:rs4422948
1   836529  rs1192410597    C   G   .   PASS    .   ES:SE:LP:SS:ID  -0.1283:0.1232:0.526513:60554:rs1192410597
1   836684  rs74460547  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.5731:0.3916:0.843754:58712:rs74460547
1   836896  rs28705752  T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0885:0.1143:0.357832:60095:rs28705752
1   836924  rs72890788  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.1295:0.1232:0.532984:60554:rs72890788
1   837192  rs57494724  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.1034:0.2291:0.185819:58712:rs57494724
1   837657  rs149737509 G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0664:0.3771:0.0654006:58712:rs149737509
1   838387  rs4970384   T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.1375:0.1227:0.58054:60554:rs4970384
1   838555  rs4970383   C   A   .   PASS    .   ES:SE:LP:SS:ID  -0.1085:0.1151:0.461175:60095:rs4970383
1   838665  rs28678693  T   C   .   PASS    .   ES:SE:LP:SS:ID  0.3225:0.3576:0.435216:58712:rs28678693
1   838732  rs952157075 G   A   .   PASS    .   ES:SE:LP:SS:ID  0.3256:0.3585:0.439257:58712:rs952157075
1   838890  rs28437697  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.3261:0.3577:0.441411:58712:rs28437697
1   838916  rs28539852  A   T   .   PASS    .   ES:SE:LP:SS:ID  0.3434:0.3582:0.471469:58712:rs28539852