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/dev/ieu-a-import/processed/ieu-a-96/ieu-a-96.vcf.gz \
--ref-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-96/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Tue Feb  4 20:37:28 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-96/ieu-a-96.vcf.gz ...
Read summary statistics for 2761247 SNPs.
Reading reference panel LD Score from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/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 /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1272650 SNPs remain.
After merging with regression SNP LD, 1272650 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.2606 (0.0133)
Lambda GC: 1.0142
Mean Chi^2: 1.1137
Intercept: 0.8345 (0.0054)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Tue Feb  4 20:38:01 2020
Total time elapsed: 32.84s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9172,
    "inflation_factor": 1,
    "mean_EFFECT": -0.0009,
    "n": 60586.9,
    "n_snps": 2761247,
    "n_clumped_hits": 42,
    "n_p_sig": 1321,
    "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": 501696,
    "n_miss_AF_reference": 27839,
    "n_est": 59379.5692,
    "ratio_se_n": 0.99,
    "mean_diff": 0,
    "ratio_diff": 3.6349,
    "sd_y_est1": 1.0885,
    "sd_y_est2": 1.0776,
    "r2_sum1": 0.0342,
    "r2_sum2": 0.0289,
    "r2_sum3": 0.0295,
    "r2_sum4": 0.0285,
    "ldsc_nsnp_merge_refpanel_ld": 1272650,
    "ldsc_nsnp_merge_regression_ld": 1272650,
    "ldsc_observed_scale_h2_beta": 0.2606,
    "ldsc_observed_scale_h2_se": 0.0133,
    "ldsc_intercept_beta": 0.8345,
    "ldsc_intercept_se": 0.0054,
    "ldsc_lambda_gc": 1.0142,
    "ldsc_mean_chisq": 1.1137,
    "ldsc_ratio": -1.4556
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n FALSE
is_snpid_non_unique FALSE
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 numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 42 0 2761247 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 8.615528e+00 5.690172e+00 1.0000 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA 7.880965e+07 5.590732e+07 6689.0000 3.245276e+07 6.996936e+07 1.144380e+08 2.492114e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -8.680000e-04 2.937105e-01 -360.0000 -6.800000e-03 -2.000000e-04 6.300000e-03 2.700000e+02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 2.226530e-02 3.032592e-01 0.0059 6.600000e-03 8.200000e-03 1.300000e-02 2.900000e+02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.977746e-01 2.921432e-01 0.0000 2.399999e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.976344e-01 2.920956e-01 0.0000 2.433450e-01 4.996758e-01 7.508943e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 501696 0.8183082 NA NA NA NA NA 3.565765e-01 2.754927e-01 0.0083 1.167000e-01 2.917000e-01 5.583000e-01 9.917000e-01 ▇▅▃▂▂
numeric AF_reference 27839 0.9899180 NA NA NA NA NA 3.526980e-01 2.621854e-01 0.0000 1.317890e-01 2.863420e-01 5.395370e-01 1.000000e+00 ▇▆▃▂▂
numeric N 0 1.0000000 NA NA NA NA NA 5.336174e+04 1.663008e+04 112.0000 5.857140e+04 6.048800e+04 6.054120e+04 6.058690e+04 ▁▁▁▁▇

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 564477 rs6650104 A G -0.0740 0.140 0.5999997 0.5971028 NA 0.0003994 1699.56
1 721290 rs12565286 G C -0.0240 0.025 0.3400001 0.3370552 NA 0.0371406 21734.00
1 723819 rs11804171 T A -0.0230 0.025 0.3599996 0.3575728 NA 0.1345850 21746.00
1 723891 rs2977670 G C 0.0430 0.040 0.2800000 0.2823747 NA 0.7799520 11147.00
1 740857 rs12082473 G A 0.0014 0.330 1.0000000 0.9966150 NA 0.0585064 2008.00
1 752566 rs3094315 G A -0.0022 0.010 0.8300000 0.8258712 NA 0.7182510 37018.80
1 752721 rs3131972 A G 0.0210 0.050 0.6700003 0.6744855 NA 0.6533550 1699.75
1 753541 rs2073813 G A 0.0150 0.013 0.2700001 0.2485632 NA 0.3019170 23777.00
1 754182 rs3131969 A G 0.0180 0.052 0.7300002 0.7292271 NA 0.6785140 1699.92
1 754192 rs3131968 A G 0.0023 0.020 0.9100000 0.9084451 NA 0.6785140 14507.10
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51211392 rs3888396 T C 0.0029 0.0360 0.9400001 0.9357954 NA 0.1641370 10434.0
22 51212875 rs2238837 A C -0.0140 0.0100 0.2000000 0.1615133 0.3917 0.3724040 26329.9
22 51213613 rs34726907 C T -0.1200 0.0500 0.0120000 0.0163951 NA 0.1727240 1873.0
22 51219006 rs28729663 G A -0.0210 0.0390 0.5800000 0.5902585 NA 0.2052720 2882.0
22 51229805 rs9616985 T C -0.0250 0.0220 0.2399999 0.2558044 0.0917 0.0730831 24763.0
23 35921591 rs2204667 C G 0.0086 0.0083 0.2999998 0.3001347 0.1667 NA 60534.1
23 51666786 rs14115 A G 0.0150 0.0140 0.2700001 0.2839768 0.0250 NA 60279.3
23 70163799 rs1626496 A C -0.0240 0.0120 0.0439997 0.0455003 0.0750 NA 59703.4
23 91415872 rs6562597 G A 0.0160 0.0220 0.4799997 0.4670589 0.0083 0.0021192 58609.9
23 118495837 rs12882977 G A -0.0038 0.0061 0.5400003 0.5333168 0.5167 0.2307280 60542.7

bcf preview

1   564477  rs6650104   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.074:0.14:0.221849:1699.56:rs6650104
1   721290  rs12565286  G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.024:0.025:0.468521:21734:rs12565286
1   723819  rs11804171  T   A   .   PASS    .   ES:SE:LP:SS:ID  -0.023:0.025:0.443698:21746:rs11804171
1   723891  rs2977670   G   C   .   PASS    .   ES:SE:LP:SS:ID  0.043:0.04:0.552842:11147:rs2977670
1   740857  rs12082473  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0014:0.33:-0:2008:rs12082473
1   752566  rs3094315   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0022:0.01:0.0809219:37018.8:rs3094315
1   752721  rs3131972   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.021:0.05:0.173925:1699.75:rs3131972
1   753541  rs2073813   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.015:0.013:0.568636:23777:rs2073813
1   754182  rs3131969   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.018:0.052:0.136677:1699.92:rs3131969
1   754192  rs3131968   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0023:0.02:0.0409586:14507.1:rs3131968
1   760912  rs1048488   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.022:0.049:0.180456:1699.94:rs1048488
1   761732  rs2286139   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.04:0.034:0.619789:3731:rs2286139
1   768448  rs12562034  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.018:0.021:0.39794:18624.3:rs12562034
1   775659  rs2905035   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0091:0.011:0.387216:38295.1:rs2905035
1   776546  rs12124819  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.026:0.024:0.585027:6438.41:rs12124819
1   777122  rs2980319   A   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0095:0.011:0.408935:38293.9:rs2980319
1   779322  rs4040617   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.011:0.011:0.481486:38203.5:rs4040617
1   780785  rs2977612   T   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0088:0.011:0.376751:38279.8:rs2977612
1   784023  rs17160939  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.14:0.074:1.24413:5651.32:rs17160939
1   785050  rs2905062   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0075:0.011:0.309804:38259.7:rs2905062
1   785989  rs2980300   T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0046:0.011:0.173925:38212.7:rs2980300
1   798026  rs4951864   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.016:0.021:0.366532:18382.4:rs4951864
1   798801  rs12132517  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.016:0.021:0.346787:18381.5:rs12132517
1   798959  rs11240777  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.011:0.013:0.420216:21909.7:rs11240777
1   799463  rs4245756   T   C   .   PASS    .   ES:SE:LP:SS:ID  0.85:0.58:0.853872:1699.99:rs4245756
1   838555  rs4970383   C   A   .   PASS    .   ES:SE:LP:SS:ID  0.021:0.042:0.207608:1699.99:rs4970383
1   846808  rs4475691   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.013:0.03:0.173925:4643.97:rs4475691
1   846864  rs950122    G   C   .   PASS    .   ES:SE:LP:SS:ID  0.018:0.074:0.091515:1697.92:rs950122
1   854250  rs7537756   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0069:0.045:0.0555173:1699.96:rs7537756
1   861808  rs13302982  A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.048:0.11:0.187087:1700:rs13302982
1   863124  rs4040604   G   T   .   PASS    .   ES:SE:LP:SS:ID  -0.017:0.11:0.0604807:1699.99:rs4040604
1   870645  rs28576697  T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.013:0.04:0.124939:1699.57:rs28576697
1   879317  rs7523549   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.067:0.11:0.267606:1699.91:rs7523549
1   880238  rs3748592   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.029:0.08:0.142668:1699.92:rs3748592
1   880390  rs3748593   C   A   .   PASS    .   ES:SE:LP:SS:ID  -0.032:0.11:0.113509:1700:rs3748593
1   882033  rs2272756   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0003:0.042:0.00436481:1699.98:rs2272756
1   882803  rs2340582   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.021:0.079:0.102373:1699.98:rs2340582
1   884815  rs4246503   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.019:0.079:0.091515:1699.99:rs4246503
1   886384  rs3748594   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.032:0.11:0.113509:1699.99:rs3748594
1   887162  rs17160698  T   C   .   PASS    .   ES:SE:LP:SS:ID  0.16:0.31:0.21467:1699.94:rs17160698
1   887560  rs3748595   A   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0039:0.075:0.0177288:1699.93:rs3748595
1   888659  rs3748597   T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.088:0.043:1.38722:3746:rs3748597
1   891945  rs13303106  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.021:0.038:0.236572:1699.98:rs13303106
1   893981  rs28415373  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.35:0.23:0.886057:1699.91:rs28415373
1   894573  rs13303010  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.045:0.061:0.337242:1699.75:rs13303010
1   900505  rs28705211  G   C   .   PASS    .   ES:SE:LP:SS:ID  0.012:0.025:0.200659:4645.43:rs28705211
1   900730  rs3935066   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.081:0.062:0.721246:1699.76:rs3935066
1   903104  rs6696281   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.07:0.094:0.337242:1699.98:rs6696281
1   904165  rs28391282  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.21:0.34:0.259637:1700:rs28391282
1   908414  rs28504611  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.15:0.11:0.744727:1699.88:rs28504611