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-100/ieu-a-100.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-100/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 19:32:23 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-100/ieu-a-100.vcf.gz ...
Read summary statistics for 2724275 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, 1271652 SNPs remain.
After merging with regression SNP LD, 1271652 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1588 (0.0139)
Lambda GC: 1.006
Mean Chi^2: 1.0232
Intercept: 0.9314 (0.0052)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Tue Feb  4 19:32:57 2020
Total time elapsed: 34.48s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9172,
    "inflation_factor": 1,
    "mean_EFFECT": -0.0003,
    "n": 32932.2,
    "n_snps": 2724275,
    "n_clumped_hits": 2,
    "n_p_sig": 35,
    "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": 464707,
    "n_miss_AF_reference": 26418,
    "n_est": 31959.9184,
    "ratio_se_n": 0.9851,
    "mean_diff": 2.3428e-06,
    "ratio_diff": 0.3783,
    "sd_y_est1": 1.0422,
    "sd_y_est2": 1.0267,
    "r2_sum1": 0.0023,
    "r2_sum2": 0.0022,
    "r2_sum3": 0.0022,
    "r2_sum4": 0.0022,
    "ldsc_nsnp_merge_refpanel_ld": 1271652,
    "ldsc_nsnp_merge_regression_ld": 1271652,
    "ldsc_observed_scale_h2_beta": 0.1588,
    "ldsc_observed_scale_h2_se": 0.0139,
    "ldsc_intercept_beta": 0.9314,
    "ldsc_intercept_se": 0.0052,
    "ldsc_lambda_gc": 1.006,
    "ldsc_mean_chisq": 1.0232,
    "ldsc_ratio": -2.9569
}

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 2724275 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 8.614093e+00 5.687541e+00 1.0000 4.00000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA 7.881021e+07 5.589193e+07 6689.0000 3.24569e+07 7.000891e+07 1.144228e+08 2.492114e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -2.982000e-04 4.169229e-01 -190.0000 -8.40000e-03 -1.000000e-04 8.100000e-03 4.300000e+02 ▁▇▁▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 2.394080e-02 5.612692e-01 0.0078 8.50000e-03 1.000000e-02 1.600000e-02 5.200000e+02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.984973e-01 2.892754e-01 0.0000 2.50000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.982761e-01 2.893976e-01 0.0000 2.46907e-01 4.994695e-01 7.482197e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 464707 0.8294199 NA NA NA NA NA 3.565768e-01 2.754925e-01 0.0083 1.16700e-01 2.917000e-01 5.583000e-01 9.917000e-01 ▇▅▃▂▂
numeric AF_reference 26418 0.9903027 NA NA NA NA NA 3.550142e-01 2.607430e-01 0.0000 1.35383e-01 2.899360e-01 5.415340e-01 1.000000e+00 ▇▆▃▃▂
numeric N 0 1.0000000 NA NA NA NA NA 2.958299e+04 8.430961e+03 118.0000 3.23068e+04 3.292370e+04 3.292940e+04 3.293220e+04 ▁▁▁▁▇

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 721290 rs12565286 G C -0.0380 0.034 0.2700001 0.2637178 NA 0.0371406 13613.00
1 723819 rs11804171 T A -0.0360 0.034 0.2999998 0.2896802 NA 0.1345850 13627.00
1 723891 rs2977670 G C 0.0230 0.046 0.6200004 0.6170751 NA 0.7799520 7109.00
1 752566 rs3094315 G A -0.0120 0.014 0.3900004 0.3913659 NA 0.7182510 22261.00
1 752721 rs3131972 A G 0.0190 0.050 0.6999999 0.7039454 NA 0.6533550 611.91
1 753541 rs2073813 G A 0.0039 0.020 0.8499999 0.8453929 NA 0.3019170 14341.00
1 754182 rs3131969 A G 0.0240 0.052 0.6400000 0.6444123 NA 0.6785140 611.97
1 754192 rs3131968 A G -0.0370 0.023 0.1100001 0.1076829 NA 0.6785140 9161.75
1 760912 rs1048488 C T 0.0280 0.050 0.5700002 0.5754794 NA 0.7340260 611.97
1 761732 rs2286139 C T -0.0990 0.045 0.0269998 0.0278069 NA 0.6257990 1975.00
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51211392 rs3888396 T C 0.0036 0.049 0.9400001 0.9414326 NA 0.1641370 8148.0
22 51212875 rs2238837 A C -0.0120 0.013 0.3700002 0.3559671 0.3917 0.3724040 20749.6
22 51213613 rs34726907 C T -0.0960 0.052 0.0659994 0.0648699 NA 0.1727240 1860.0
22 51219006 rs28729663 G A -0.0660 0.047 0.1700000 0.1602428 NA 0.2052720 1990.0
22 51229805 rs9616985 T C -0.0110 0.031 0.7099994 0.7227104 0.0917 0.0730831 18606.9
23 35921591 rs2204667 C G 0.0130 0.011 0.2399999 0.2372779 0.1667 NA 32925.4
23 51666786 rs14115 A G -0.0007 0.017 0.9699999 0.9671552 0.0250 NA 32842.8
23 70163799 rs1626496 A C -0.0170 0.016 0.2900000 0.2880088 0.0750 NA 32931.2
23 91415872 rs6562597 G A -0.0160 0.033 0.6400000 0.6277838 0.0083 0.0021192 31026.5
23 118495837 rs12882977 G A -0.0031 0.008 0.6999999 0.6983861 0.5167 0.2307280 32928.6

bcf preview

1   721290  rs12565286  G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.038:0.034:0.568636:13613:rs12565286
1   723819  rs11804171  T   A   .   PASS    .   ES:SE:LP:SS:ID  -0.036:0.034:0.522879:13627:rs11804171
1   723891  rs2977670   G   C   .   PASS    .   ES:SE:LP:SS:ID  0.023:0.046:0.207608:7109:rs2977670
1   752566  rs3094315   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.012:0.014:0.408935:22261:rs3094315
1   752721  rs3131972   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.019:0.05:0.154902:611.91:rs3131972
1   753541  rs2073813   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0039:0.02:0.0705811:14341:rs2073813
1   754182  rs3131969   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.024:0.052:0.19382:611.97:rs3131969
1   754192  rs3131968   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.037:0.023:0.958607:9161.75:rs3131968
1   760912  rs1048488   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.028:0.05:0.244125:611.97:rs1048488
1   761732  rs2286139   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.099:0.045:1.56864:1975:rs2286139
1   768448  rs12562034  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.02:0.027:0.346787:13210:rs12562034
1   775659  rs2905035   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.019:0.015:0.657577:23501.7:rs2905035
1   776546  rs12124819  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.026:0.019:0.744727:4759.41:rs12124819
1   777122  rs2980319   A   T   .   PASS    .   ES:SE:LP:SS:ID  -0.02:0.015:0.721246:23502.6:rs2980319
1   779322  rs4040617   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.022:0.016:0.79588:22762.2:rs4040617
1   780785  rs2977612   T   A   .   PASS    .   ES:SE:LP:SS:ID  -0.021:0.015:0.769551:23501.6:rs2977612
1   784023  rs17160939  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.13:0.07:1.20066:2857.98:rs17160939
1   785050  rs2905062   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.022:0.015:0.79588:23501.5:rs2905062
1   785989  rs2980300   T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.019:0.015:0.677781:23429.5:rs2980300
1   798026  rs4951864   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.024:0.027:0.420216:13210.5:rs4951864
1   798801  rs12132517  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.02:0.027:0.337242:13175.3:rs12132517
1   798959  rs11240777  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.012:0.017:0.30103:14706.9:rs11240777
1   838555  rs4970383   C   A   .   PASS    .   ES:SE:LP:SS:ID  0.06:0.046:0.721246:611.99:rs4970383
1   846808  rs4475691   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.012:0.021:0.251812:3060.98:rs4475691
1   846864  rs950122    G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.018:0.09:0.0757207:611.24:rs950122
1   854250  rs7537756   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.088:0.051:1.07058:612:rs7537756
1   861808  rs13302982  A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.08:0.11:0.337242:612:rs13302982
1   863124  rs4040604   G   T   .   PASS    .   ES:SE:LP:SS:ID  -0.064:0.11:0.259637:612:rs4040604
1   870645  rs28576697  T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.028:0.045:0.275724:611.94:rs28576697
1   879317  rs7523549   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.026:0.11:0.0861861:611.93:rs7523549
1   880238  rs3748592   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.096:0.087:0.568636:612:rs3748592
1   880390  rs3748593   C   A   .   PASS    .   ES:SE:LP:SS:ID  0.043:0.13:0.136677:612:rs3748593
1   882033  rs2272756   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.027:0.046:0.244125:612:rs2272756
1   882803  rs2340582   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.083:0.087:0.468521:611.99:rs2340582
1   884815  rs4246503   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.096:0.087:0.568636:612:rs4246503
1   886384  rs3748594   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.03:0.12:0.091515:612:rs3748594
1   887560  rs3748595   A   C   .   PASS    .   ES:SE:LP:SS:ID  -0.13:0.082:0.920819:611.98:rs3748595
1   888659  rs3748597   T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.12:0.075:0.920819:1993:rs3748597
1   891945  rs13303106  A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0059:0.042:0.05061:611.99:rs13303106
1   894573  rs13303010  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.11:0.07:1:611.99:rs13303010
1   900505  rs28705211  G   C   .   PASS    .   ES:SE:LP:SS:ID  0.013:0.017:0.337242:3061.73:rs28705211
1   900730  rs3935066   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.098:0.073:0.744727:612:rs3935066
1   903104  rs6696281   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.086:0.11:0.356547:612:rs6696281
1   908414  rs28504611  C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.068:0.12:0.251812:611.95:rs28504611
1   916834  rs6694632   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.015:0.04:0.148742:612:rs6694632
1   918384  rs13303118  G   T   .   PASS    .   ES:SE:LP:SS:ID  0.0033:0.017:0.0757207:2857.07:rs13303118
1   918573  rs2341354   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.025:0.034:0.337242:2004:rs2341354
1   924898  rs6665000   C   A   .   PASS    .   ES:SE:LP:SS:ID  -0.068:0.12:0.236572:612:rs6665000
1   926431  rs4970403   A   T   .   PASS    .   ES:SE:LP:SS:ID  -0.16:0.097:1.04096:1999:rs4970403
1   927309  rs2341362   T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.068:0.12:0.236572:612:rs2341362