Summary

Summary {data-width=650}

Manhattan plot

manhattan_plot

manhattan_plot

QQ plot

qq_plot

qq_plot

AF plot

af_plot

af_plot

P-Z plot

pz_plot

pz_plot

beta_std 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 /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6591/UKB-b-6591_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6591/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6591/UKB-b-6591_data.vcf.gz ...
Read summary statistics for 9851866 SNPs.
Dropped 14738 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1289166 SNPs remain.
After merging with regression SNP LD, 1289166 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.143 (0.0043)
Lambda GC: 1.8732
Mean Chi^2: 2.2424
Intercept: 1.0754 (0.0123)
Ratio: 0.0607 (0.0099)
Analysis finished at Thu Oct 17 14:42:13 2019
Total time elapsed: 1.0m:53.94s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9499,
    "inflation_factor": 1.557,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 200,
    "n_p_sig": 21761,
    "n_mono": 0,
    "n_ns": 0,
    "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": 184849,
    "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": 1289166,
    "ldsc_nsnp_merge_regression_ld": 1289166,
    "ldsc_observed_scale_h2_beta": 0.143,
    "ldsc_observed_scale_h2_se": 0.0043,
    "ldsc_intercept_beta": 1.0754,
    "ldsc_intercept_se": 0.0123,
    "ldsc_lambda_gc": 1.8732,
    "ldsc_mean_chisq": 2.2424,
    "ldsc_ratio": 0.0607
}

Flags

name value
af_correlation FALSE
inflation_factor TRUE
n TRUE
is_snpid_non_unique FALSE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq TRUE
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 0 1.0000000 3 58 0 9837196 0 NA NA 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 NA NA
character ALT 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 9851866 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 8.622825e+00 5.748290e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.886027e+07 5.628334e+07 828.0000000 3.259061e+07 6.948835e+07 1.145912e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.355000e-04 1.125720e-02 -0.1779420 -4.058200e-03 -4.510000e-05 3.919900e-03 1.699590e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 7.436700e-03 7.044700e-03 0.0020797 2.546500e-03 4.270400e-03 9.853000e-03 1.084310e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.278522e-01 3.065310e-01 0.0000000 1.400000e-01 4.000000e-01 6.899999e-01 1.000000e+00 ▇▅▅▅▅
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.278533e-01 3.065093e-01 0.0000000 1.444544e-01 3.993011e-01 6.926932e-01 9.999998e-01 ▇▅▅▅▅
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.035066e-01 2.568662e-01 0.0009520 1.316800e-02 7.790200e-02 3.164290e-01 9.990440e-01 ▇▂▁▁▁
numeric AF_reference 184849 0.9812372 NA NA NA NA NA NA NA 2.068392e-01 2.482924e-01 0.0000000 1.198080e-02 9.984030e-02 3.202880e-01 1.000000e+00 ▇▂▁▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 49298 rs200943160 T C -0.0024973 0.0038237 0.5099998 0.5136753 0.623733 0.7821490 NA
1 54676 rs2462492 C T -0.0023252 0.0037870 0.5400003 0.5392276 0.400470 NA NA
1 86028 rs114608975 T C 0.0003523 0.0060506 0.9500000 0.9535730 0.103586 0.0277556 NA
1 91536 rs6702460 G T -0.0019204 0.0037290 0.6100002 0.6065559 0.456974 0.4207270 NA
1 234313 rs8179466 C T -0.0017244 0.0073549 0.8100000 0.8146316 0.074513 NA NA
1 534192 rs6680723 C T -0.0042445 0.0042591 0.3200000 0.3189689 0.241032 NA NA
1 546697 rs12025928 A G -0.0054398 0.0053076 0.3100002 0.3054059 0.913285 NA NA
1 693731 rs12238997 A G -0.0002353 0.0035652 0.9500000 0.9473818 0.116471 0.1417730 NA
1 705882 rs72631875 G A 0.0058198 0.0052316 0.2700001 0.2659503 0.067286 0.0315495 NA
1 706368 rs55727773 A G -0.0000593 0.0026444 0.9800000 0.9821166 0.515753 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0004058 0.0055687 0.9400001 0.9419094 0.041911 0.0473243 NA
22 51219766 rs182321900 C T 0.0245630 0.0260644 0.3500000 0.3459897 0.001923 NA NA
22 51220146 rs868950473 C T 0.0242429 0.0258266 0.3500000 0.3478953 0.001971 NA NA
22 51221190 rs369304721 G A 0.0075598 0.0055588 0.1700000 0.1738392 0.049664 NA NA
22 51221731 rs115055839 T C 0.0052875 0.0041573 0.2000000 0.2034239 0.073180 0.0625000 NA
22 51222100 rs114553188 G T -0.0066634 0.0048915 0.1700000 0.1731239 0.054448 0.0880591 NA
22 51223637 rs375798137 G A -0.0064973 0.0049154 0.1900002 0.1862248 0.054076 0.0788738 NA
22 51229805 rs9616985 T C 0.0048957 0.0041713 0.2399999 0.2405282 0.073043 0.0730831 NA
22 51232488 rs376461333 A G -0.0112226 0.0098268 0.2500000 0.2534383 0.020035 NA NA
22 51237063 rs3896457 T C -0.0000792 0.0025506 0.9800000 0.9752398 0.298063 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623733 ES:SE:LP:AF:ID  -0.00249733:0.00382366:0.29243:0.623733:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.40047  ES:SE:LP:AF:ID  -0.00232516:0.00378702:0.267606:0.40047:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103586 ES:SE:LP:AF:ID  0.00035227:0.00605062:0.0222764:0.103586:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456974 ES:SE:LP:AF:ID  -0.00192043:0.00372902:0.21467:0.456974:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074513 ES:SE:LP:AF:ID  -0.00172439:0.00735488:0.091515:0.074513:rs8179466
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1   546697  rs12025928  A   G   .   PASS    AF=0.913285 ES:SE:LP:AF:ID  -0.00543981:0.0053076:0.508638:0.913285:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116471 ES:SE:LP:AF:ID  -0.000235286:0.00356521:0.0222764:0.116471:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067286 ES:SE:LP:AF:ID  0.00581977:0.00523155:0.568636:0.067286:rs72631875
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1   714596  rs149887893 T   C   .   PASS    AF=0.033011 ES:SE:LP:AF:ID  -0.00338719:0.00666488:0.21467:0.033011:rs149887893
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1   717587  rs144155419 G   A   .   PASS    AF=0.016381 ES:SE:LP:AF:ID  0.00133125:0.00935564:0.05061:0.016381:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036998 ES:SE:LP:AF:ID  -0.00187399:0.00600556:0.119186:0.036998:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03709  ES:SE:LP:AF:ID  -0.00254969:0.00598552:0.173925:0.03709:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101205 ES:SE:LP:AF:ID  0.00855148:0.00436186:1.30103:0.101205:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959112 ES:SE:LP:AF:ID  0.00133145:0.00577563:0.0861861:0.959112:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031463 ES:SE:LP:AF:ID  0.000327319:0.0104813:0.00877392:0.031463:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053312 ES:SE:LP:AF:ID  -0.00254852:0.00832739:0.119186:0.053312:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.0366   ES:SE:LP:AF:ID  -0.00204858:0.00602485:0.136677:0.0366:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036915 ES:SE:LP:AF:ID  -0.000632257:0.00596996:0.0362122:0.036915:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843063 ES:SE:LP:AF:ID  0.000619807:0.00309159:0.0757207:0.843063:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056038 ES:SE:LP:AF:ID  -0.0027373:0.00500088:0.236572:0.056038:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122469 ES:SE:LP:AF:ID  -0.000710612:0.00338252:0.0809219:0.122469:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025731 ES:SE:LP:AF:ID  -0.0170259:0.00832793:1.38722:0.025731:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121707 ES:SE:LP:AF:ID  -0.000606896:0.00338385:0.0655015:0.121707:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13255  ES:SE:LP:AF:ID  -0.000565766:0.00333476:0.0604807:0.13255:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011102 ES:SE:LP:AF:ID  -0.00357823:0.0121562:0.113509:0.011102:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005703 ES:SE:LP:AF:ID  -0.0106241:0.0156547:0.30103:0.005703:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002243 ES:SE:LP:AF:ID  0.00225819:0.026593:0.0315171:0.002243:rs112573343
1   746189  rs139221807 A   G   .   PASS    AF=0.001022 ES:SE:LP:AF:ID  -0.0213066:0.0433271:0.207608:0.001022:rs139221807
1   752478  rs146277091 G   A   .   PASS    AF=0.036821 ES:SE:LP:AF:ID  -0.00317334:0.00591025:0.229148:0.036821:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838832 ES:SE:LP:AF:ID  -7.12827e-05:0.00299366:0.00877392:0.838832:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838452 ES:SE:LP:AF:ID  -0.000164173:0.0029904:0.0177288:0.838452:rs3131972
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1   753541  rs2073813   G   A   .   PASS    AF=0.130007 ES:SE:LP:AF:ID  0.000767266:0.00321462:0.091515:0.130007:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037331 ES:SE:LP:AF:ID  -0.00317378:0.00581067:0.236572:0.037331:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037578 ES:SE:LP:AF:ID  -0.00276141:0.00577355:0.200659:0.037578:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869008 ES:SE:LP:AF:ID  -0.000751496:0.00320205:0.091515:0.869008:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869108 ES:SE:LP:AF:ID  -0.000705347:0.00320337:0.0809219:0.869108:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037533 ES:SE:LP:AF:ID  -0.00303624:0.00579906:0.221849:0.037533:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869011 ES:SE:LP:AF:ID  -0.000727709:0.003202:0.0861861:0.869011:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005115 ES:SE:LP:AF:ID  0.00954837:0.0164572:0.251812:0.005115:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00508  ES:SE:LP:AF:ID  0.0111075:0.0165009:0.30103:0.00508:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837905 ES:SE:LP:AF:ID  0.00019717:0.0029823:0.0222764:0.837905:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037542 ES:SE:LP:AF:ID  -0.00316622:0.0058074:0.229148:0.037542:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838534 ES:SE:LP:AF:ID  0.000352037:0.00299068:0.0409586:0.838534:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013809 ES:SE:LP:AF:ID  0.0107077:0.0104236:0.522879:0.013809:rs181660517