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-106/ieu-a-106.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-106/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Wed Feb  5 10:58:47 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-106/ieu-a-106.vcf.gz ...
Read summary statistics for 2759235 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, 1272648 SNPs remain.
After merging with regression SNP LD, 1272648 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1932 (0.0113)
Lambda GC: 1.006
Mean Chi^2: 1.0594
Intercept: 0.8621 (0.0052)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Wed Feb  5 10:59:21 2020
Total time elapsed: 33.73s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9172,
    "inflation_factor": 1,
    "mean_EFFECT": -0.0003,
    "n": 58351.7,
    "n_snps": 2759235,
    "n_clumped_hits": 9,
    "n_p_sig": 401,
    "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": 499684,
    "n_miss_AF_reference": 27804,
    "n_est": 56703.6193,
    "ratio_se_n": 0.9858,
    "mean_diff": 0,
    "ratio_diff": 2.5072,
    "sd_y_est1": 1.1237,
    "sd_y_est2": 1.1077,
    "r2_sum1": 0.0094,
    "r2_sum2": 0.0074,
    "r2_sum3": 0.0077,
    "r2_sum4": 0.0077,
    "ldsc_nsnp_merge_refpanel_ld": 1272648,
    "ldsc_nsnp_merge_regression_ld": 1272648,
    "ldsc_observed_scale_h2_beta": 0.1932,
    "ldsc_observed_scale_h2_se": 0.0113,
    "ldsc_intercept_beta": 0.8621,
    "ldsc_intercept_se": 0.0052,
    "ldsc_lambda_gc": 1.006,
    "ldsc_mean_chisq": 1.0594,
    "ldsc_ratio": -2.3215
}

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 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 numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 42 0 2759235 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 8.615770e+00 5.690239e+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 7.880929e+07 5.590576e+07 6689.0000 3.245238e+07 6.997215e+07 1.144397e+08 2.492114e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -3.041000e-04 5.383510e-02 -4.0000 -7.000000e-03 -1.000000e-04 6.800000e-03 3.000000e+00 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 2.253830e-02 5.544740e-02 0.0063 7.000000e-03 8.600000e-03 1.400000e-02 7.800000e+00 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.980096e-01 2.907716e-01 0.0000 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.979314e-01 2.907129e-01 0.0000 2.433450e-01 4.987419e-01 7.499161e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 499684 0.8189049 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 27804 0.9899233 NA NA NA NA NA 3.528276e-01 2.621023e-01 0.0000 1.319890e-01 2.865420e-01 5.397360e-01 1.000000e+00 ▇▆▃▂▂
numeric N 0 1.0000000 NA NA NA NA NA 5.154263e+04 1.604436e+04 112.0000 5.650905e+04 5.830810e+04 5.832180e+04 5.835170e+04 ▁▁▁▁▇

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 564477 rs6650104 A G 0.0780 0.140 0.5700002 0.5774298 NA 0.0003994 1702.56
1 721290 rs12565286 G C 0.0081 0.029 0.7800007 0.7800067 NA 0.0371406 21727.00
1 723819 rs11804171 T A 0.0083 0.029 0.7800007 0.7747197 NA 0.1345850 21742.00
1 723891 rs2977670 G C 0.0260 0.040 0.5199996 0.5156922 NA 0.7799520 11157.00
1 740857 rs12082473 G A -0.0046 0.360 0.9900000 0.9898051 NA 0.0585064 2008.00
1 752566 rs3094315 G A 0.0019 0.011 0.8600001 0.8628658 NA 0.7182510 36541.80
1 752721 rs3131972 A G 0.0660 0.048 0.1700000 0.1691314 NA 0.6533550 1702.75
1 753541 rs2073813 G A -0.0009 0.015 0.9500000 0.9521556 NA 0.3019170 23271.00
1 754182 rs3131969 A G 0.0700 0.051 0.1700000 0.1698926 NA 0.6785140 1702.92
1 754192 rs3131968 A G -0.0120 0.020 0.5300002 0.5485062 NA 0.6785140 14526.10
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51212875 rs2238837 A C -0.0033 0.0120 0.7899998 0.7833162 0.3917 0.3724040 28575.90
22 51213613 rs34726907 C T -0.0880 0.0540 0.1000000 0.1031798 NA 0.1727240 1873.00
22 51219006 rs28729663 G A -0.0850 0.0480 0.0790005 0.0765884 NA 0.2052720 2004.00
22 51229805 rs9616985 T C -0.0068 0.0220 0.7600007 0.7572524 0.0917 0.0730831 27405.00
23 35921591 rs2204667 C G -0.0048 0.0087 0.5800000 0.5811374 0.1667 NA 58318.60
23 51666786 rs14115 A G 0.0040 0.0140 0.7800007 0.7750970 0.0250 NA 58137.90
23 70163799 rs1626496 A C -0.0430 0.0120 0.0004600 0.0003392 0.0750 NA 58350.90
23 91415872 rs6562597 G A -0.0027 0.0240 0.9100000 0.9104270 0.0083 0.0021192 56393.40
23 117417363 rs11541996 G A 0.1800 0.2600 0.4899999 0.4887441 NA NA 1702.87
23 118495837 rs12882977 G A 0.0005 0.0064 0.9400001 0.9377286 0.5167 0.2307280 58320.20

bcf preview

1   564477  rs6650104   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.078:0.14:0.244125:1702.56:rs6650104
1   721290  rs12565286  G   C   .   PASS    .   ES:SE:LP:SS:ID  0.0081:0.029:0.107905:21727:rs12565286
1   723819  rs11804171  T   A   .   PASS    .   ES:SE:LP:SS:ID  0.0083:0.029:0.107905:21742:rs11804171
1   723891  rs2977670   G   C   .   PASS    .   ES:SE:LP:SS:ID  0.026:0.04:0.283997:11157:rs2977670
1   740857  rs12082473  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0046:0.36:0.00436481:2008:rs12082473
1   752566  rs3094315   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0019:0.011:0.0655015:36541.8:rs3094315
1   752721  rs3131972   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.066:0.048:0.769551:1702.75:rs3131972
1   753541  rs2073813   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0009:0.015:0.0222764:23271:rs2073813
1   754182  rs3131969   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.07:0.051:0.769551:1702.92:rs3131969
1   754192  rs3131968   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.012:0.02:0.275724:14526.1:rs3131968
1   760912  rs1048488   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.069:0.048:0.823909:1702.94:rs1048488
1   761732  rs2286139   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.023:0.039:0.251812:2872:rs2286139
1   768448  rs12562034  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.011:0.022:0.207608:19502.3:rs12562034
1   775659  rs2905035   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0031:0.012:0.09691:37796.1:rs2905035
1   776546  rs12124819  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.02:0.022:0.443698:6467.91:rs12124819
1   777122  rs2980319   A   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0035:0.012:0.113509:37794.9:rs2980319
1   779322  rs4040617   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0044:0.012:0.142668:37727.5:rs4040617
1   780785  rs2977612   T   A   .   PASS    .   ES:SE:LP:SS:ID  -0.002:0.012:0.0604807:37795.8:rs2977612
1   784023  rs17160939  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.12:0.072:1:5685.02:rs17160939
1   785050  rs2905062   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.001:0.012:0.0315171:37795.7:rs2905062
1   785989  rs2980300   T   C   .   PASS    .   ES:SE:LP:SS:ID  0.0031:0.012:0.102373:37736.7:rs2980300
1   798026  rs4951864   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.014:0.023:0.275724:19373.4:rs4951864
1   798801  rs12132517  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.019:0.023:0.39794:19372.5:rs12132517
1   798959  rs11240777  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.019:0.015:0.677781:21889.7:rs11240777
1   799463  rs4245756   T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.21:0.41:0.21467:1702.99:rs4245756
1   838555  rs4970383   C   A   .   PASS    .   ES:SE:LP:SS:ID  0.016:0.041:0.161151:1702.99:rs4970383
1   846808  rs4475691   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.02:0.028:0.327902:4646.97:rs4475691
1   846864  rs950122    G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0095:0.073:0.0457575:1700.9:rs950122
1   854250  rs7537756   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0079:0.044:0.0655015:1702.96:rs7537756
1   861808  rs13302982  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.015:0.1:0.0555173:1703:rs13302982
1   863124  rs4040604   G   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0003:0.1:-0:1702.99:rs4040604
1   870645  rs28576697  T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.027:0.039:0.309804:1702.56:rs28576697
1   879317  rs7523549   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0081:0.1:0.0268721:1702.91:rs7523549
1   880238  rs3748592   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0051:0.078:0.0222764:1702.92:rs3748592
1   880390  rs3748593   C   A   .   PASS    .   ES:SE:LP:SS:ID  -0.016:0.11:0.05061:1703:rs3748593
1   882033  rs2272756   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.015:0.041:0.142668:1702.98:rs2272756
1   882803  rs2340582   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.016:0.077:0.0809219:1702.98:rs2340582
1   884815  rs4246503   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.017:0.077:0.0809219:1702.99:rs4246503
1   886384  rs3748594   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0089:0.11:0.0315171:1702.99:rs3748594
1   887162  rs17160698  T   C   .   PASS    .   ES:SE:LP:SS:ID  0.19:0.26:0.318759:1702.94:rs17160698
1   887560  rs3748595   A   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0096:0.074:0.0457575:1702.93:rs3748595
1   888659  rs3748597   T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.049:0.062:0.366532:2906:rs3748597
1   891945  rs13303106  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.019:0.037:0.21467:1702.98:rs13303106
1   893981  rs28415373  C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.036:0.2:0.0705811:1702.91:rs28415373
1   894573  rs13303010  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0066:0.059:0.0409586:1702.75:rs13303010
1   900505  rs28705211  G   C   .   PASS    .   ES:SE:LP:SS:ID  0.02:0.024:0.408935:4648.43:rs28705211
1   900730  rs3935066   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.001:0.061:0.00436481:1702.76:rs3935066
1   903104  rs6696281   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.011:0.091:0.0457575:1702.98:rs6696281
1   904165  rs28391282  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0061:0.33:0.00436481:1703:rs28391282
1   908414  rs28504611  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.2:0.11:1.19382:1702.88:rs28504611