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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    "FORMAT.1": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_1873.vcf.gz --id UKB-b:4263 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_1873.txt.gz --cohort_controls 455347 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
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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-4263/UKB-b-4263_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4263/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:06 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4263/UKB-b-4263_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: 0.0162 (0.0016)
Lambda GC: 1.327
Mean Chi^2: 1.3252
Intercept: 1.1815 (0.0081)
Ratio: 0.5581 (0.0249)
Analysis finished at Thu Oct 17 14:45:54 2019
Total time elapsed: 1.0m:47.71s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9499,
    "inflation_factor": 1.2544,
    "mean_EFFECT": 0.0002,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 6,
    "n_p_sig": 25,
    "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": 0.0162,
    "ldsc_observed_scale_h2_se": 0.0016,
    "ldsc_intercept_beta": 1.1815,
    "ldsc_intercept_se": 0.0081,
    "ldsc_lambda_gc": 1.327,
    "ldsc_mean_chisq": 1.3252,
    "ldsc_ratio": 0.5581
}
 

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 FALSE
miss_EFFECT FALSE
miss_SE FALSE
miss_PVAL FALSE
ldsc_ratio TRUE
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 1.977000e-04 8.471800e-03 -0.1114350 -2.525500e-03 4.760000e-05 2.693500e-03 1.453280e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.635700e-03 5.337000e-03 0.0015766 1.930100e-03 3.236500e-03 7.468100e-03 8.273530e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.621366e-01 2.976980e-01 0.0000000 1.900002e-01 4.500005e-01 7.199992e-01 1.000000e+00 ▇▆▆▅▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.621380e-01 2.976737e-01 0.0000000 1.946744e-01 4.472111e-01 7.196328e-01 9.999999e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.035072e-01 2.568641e-01 0.0009740 1.316800e-02 7.790800e-02 3.164530e-01 9.990220e-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 -0.0001725 0.0029009 0.9500000 0.9525884 0.623850 0.7821490 NA
1 54676 rs2462492 C T -0.0015658 0.0028732 0.5900000 0.5857830 0.400416 NA NA
1 86028 rs114608975 T C -0.0059857 0.0045944 0.1900002 0.1926376 0.103536 0.0277556 NA
1 91536 rs6702460 G T -0.0036824 0.0028297 0.1900002 0.1931491 0.456937 0.4207270 NA
1 234313 rs8179466 C T -0.0021302 0.0055772 0.6999999 0.7024942 0.074532 NA NA
1 534192 rs6680723 C T -0.0046682 0.0032320 0.1499999 0.1486420 0.240953 NA NA
1 546697 rs12025928 A G -0.0005527 0.0040307 0.8900000 0.8909403 0.913433 NA NA
1 693731 rs12238997 A G -0.0006892 0.0027082 0.8000000 0.7991312 0.116292 0.1417730 NA
1 705882 rs72631875 G A -0.0037871 0.0039685 0.3400001 0.3399393 0.067315 0.0315495 NA
1 706368 rs55727773 A G -0.0042503 0.0020062 0.0340001 0.0341284 0.515699 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0020514 0.0042132 0.6300007 0.6263296 0.041949 0.0473243 NA
22 51219766 rs182321900 C T -0.0209606 0.0197115 0.2900000 0.2876146 0.001923 NA NA
22 51220146 rs868950473 C T -0.0261613 0.0195152 0.1800002 0.1800633 0.001973 NA NA
22 51221190 rs369304721 G A -0.0028156 0.0042065 0.5000000 0.5032794 0.049715 NA NA
22 51221731 rs115055839 T C -0.0007583 0.0031464 0.8100000 0.8095441 0.073210 0.0625000 NA
22 51222100 rs114553188 G T -0.0006432 0.0037051 0.8600001 0.8621852 0.054432 0.0880591 NA
22 51223637 rs375798137 G A -0.0007511 0.0037230 0.8400000 0.8401057 0.054063 0.0788738 NA
22 51229805 rs9616985 T C -0.0006064 0.0031577 0.8499999 0.8477143 0.073047 0.0730831 NA
22 51232488 rs376461333 A G -0.0045709 0.0074426 0.5400003 0.5391193 0.020021 NA NA
22 51237063 rs3896457 T C 0.0009913 0.0019308 0.6100002 0.6076721 0.298042 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62385  ES:SE:LP:AF:ID  -0.000172476:0.00290087:0.0222764:0.62385:rs10399793
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1   91536   rs6702460   G   T   .   PASS    AF=0.456937 ES:SE:LP:AF:ID  -0.00368237:0.00282972:0.721246:0.456937:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074532 ES:SE:LP:AF:ID  -0.00213024:0.00557719:0.154902:0.074532:rs8179466
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1   693731  rs12238997  A   G   .   PASS    AF=0.116292 ES:SE:LP:AF:ID  -0.000689155:0.00270818:0.09691:0.116292:rs12238997
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1   717587  rs144155419 G   A   .   PASS    AF=0.016417 ES:SE:LP:AF:ID  -0.00381013:0.00709374:0.229148:0.016417:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036958 ES:SE:LP:AF:ID  0.00730325:0.00455917:0.958607:0.036958:rs116801199
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1   724849  rs12126395  C   A   .   PASS    AF=0.031447 ES:SE:LP:AF:ID  0.0110825:0.00795261:0.79588:0.031447:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053278 ES:SE:LP:AF:ID  -0.00373752:0.0063226:0.259637:0.053278:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036573 ES:SE:LP:AF:ID  0.00687013:0.00457293:0.886057:0.036573:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036891 ES:SE:LP:AF:ID  0.00681392:0.00453122:0.886057:0.036891:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843282 ES:SE:LP:AF:ID  -0.00163027:0.002347:0.309804:0.843282:rs4951859
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1   732989  rs369030935 C   T   .   PASS    AF=0.025739 ES:SE:LP:AF:ID  0.00378655:0.0063154:0.259637:0.025739:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121513 ES:SE:LP:AF:ID  -0.000514449:0.00257009:0.0757207:0.121513:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132282 ES:SE:LP:AF:ID  0.000385182:0.00253327:0.0555173:0.132282:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011113 ES:SE:LP:AF:ID  -0.00161383:0.00921529:0.0655015:0.011113:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005687 ES:SE:LP:AF:ID  -0.0132486:0.0119014:0.568636:0.005687:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002259 ES:SE:LP:AF:ID  0.0335832:0.0200393:1.02687:0.002259:rs112573343
1   746189  rs139221807 A   G   .   PASS    AF=0.00102  ES:SE:LP:AF:ID  0.02632:0.0329726:0.376751:0.00102:rs139221807
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1   752721  rs3131972   A   G   .   PASS    AF=0.838639 ES:SE:LP:AF:ID  -0.00222888:0.00227053:0.481486:0.838639:rs3131972
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1   753541  rs2073813   G   A   .   PASS    AF=0.129824 ES:SE:LP:AF:ID  0.00102732:0.0024416:0.173925:0.129824:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037323 ES:SE:LP:AF:ID  0.00556462:0.00440889:0.677781:0.037323:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037562 ES:SE:LP:AF:ID  0.00565128:0.00438136:0.69897:0.037562:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869175 ES:SE:LP:AF:ID  -0.00132698:0.0024319:0.229148:0.869175:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869272 ES:SE:LP:AF:ID  -0.00127793:0.00243284:0.221849:0.869272:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037524 ES:SE:LP:AF:ID  0.00550639:0.00440003:0.677781:0.037524:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869176 ES:SE:LP:AF:ID  -0.00133155:0.00243184:0.236572:0.869176:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005119 ES:SE:LP:AF:ID  0.0138538:0.0124887:0.568636:0.005119:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005085 ES:SE:LP:AF:ID  0.0140693:0.0125211:0.585027:0.005085:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838088 ES:SE:LP:AF:ID  -0.00195861:0.00226418:0.408935:0.838088:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037535 ES:SE:LP:AF:ID  0.00556435:0.00440639:0.677781:0.037535:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838721 ES:SE:LP:AF:ID  -0.00205668:0.00227057:0.431798:0.838721:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013792 ES:SE:LP:AF:ID  0.00667646:0.00791878:0.39794:0.013792:rs181660517