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-110/ieu-a-110.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-110/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 11:14:30 2020
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ieu-a-import/processed/ieu-a-110/ieu-a-110.vcf.gz ...
Read summary statistics for 2741208 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, 1272619 SNPs remain.
After merging with regression SNP LD, 1272619 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0974 (0.0137)
Lambda GC: 1.0047
Mean Chi^2: 1.0108
Intercept: 0.9506 (0.0054)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Wed Feb  5 11:15:04 2020
Total time elapsed: 33.92s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9172,
    "inflation_factor": 1,
    "mean_EFFECT": -0.0004,
    "n": 34653.6,
    "n_snps": 2741208,
    "n_clumped_hits": 1,
    "n_p_sig": 8,
    "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": 481646,
    "n_miss_AF_reference": 27073,
    "n_est": 34492.5265,
    "ratio_se_n": 0.9977,
    "mean_diff": 4.3236e-06,
    "ratio_diff": 0.4418,
    "sd_y_est1": 0.898,
    "sd_y_est2": 0.8959,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1272619,
    "ldsc_nsnp_merge_regression_ld": 1272619,
    "ldsc_observed_scale_h2_beta": 0.0974,
    "ldsc_observed_scale_h2_se": 0.0137,
    "ldsc_intercept_beta": 0.9506,
    "ldsc_intercept_se": 0.0054,
    "ldsc_lambda_gc": 1.0047,
    "ldsc_mean_chisq": 1.0108,
    "ldsc_ratio": -4.5741
}

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 2741208 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 8.614822e+00 5.688741e+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 7.880964e+07 5.590156e+07 6689.0000 3.245403e+07 6.999024e+07 1.144306e+08 2.492114e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -3.996000e-04 2.232586e-01 -170.0000 -7.000000e-03 0.000000e+00 7.000000e-03 2.700000e+01 ▁▁▁▁▇
numeric SE 0 1.0000000 NA NA NA NA NA 2.046350e-02 2.159108e-01 0.0065 7.300000e-03 8.900000e-03 1.400000e-02 1.400000e+02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.996869e-01 2.889026e-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 4.998257e-01 2.887538e-01 0.0000 2.501439e-01 5.000452e-01 7.498474e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 481646 0.8242943 NA NA NA NA NA 3.565770e-01 2.754926e-01 0.0083 1.167000e-01 2.917000e-01 5.583000e-01 9.917000e-01 ▇▅▃▂▂
numeric AF_reference 27073 0.9901237 NA NA NA NA NA 3.540227e-01 2.614280e-01 0.0000 1.337860e-01 2.883390e-01 5.407350e-01 1.000000e+00 ▇▆▃▃▂
numeric N 0 1.0000000 NA NA NA NA NA 3.071895e+04 9.177266e+03 118.0000 3.308800e+04 3.459610e+04 3.460080e+04 3.465360e+04 ▁▁▁▁▇

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 564477 rs6650104 A G 0.0030 0.190 0.9900000 0.9874023 NA 0.0003994 611.86
1 721290 rs12565286 G C 0.0200 0.027 0.4600002 0.4588507 NA 0.0371406 16035.00
1 723819 rs11804171 T A 0.0170 0.027 0.5300002 0.5289369 NA 0.1345850 16049.00
1 723891 rs2977670 G C -0.0100 0.038 0.7800007 0.7924289 NA 0.7799520 7582.97
1 740857 rs12082473 G A 0.3200 0.240 0.1800002 0.1824224 NA 0.0585064 2008.00
1 752566 rs3094315 G A -0.0035 0.011 0.7499995 0.7503470 NA 0.7182510 24211.00
1 752721 rs3131972 A G 0.0620 0.061 0.3100002 0.3094421 NA 0.6533550 611.91
1 753541 rs2073813 G A 0.0140 0.016 0.3599996 0.3815739 NA 0.3019170 15809.00
1 754182 rs3131969 A G 0.0510 0.063 0.4199997 0.4182139 NA 0.6785140 611.97
1 754192 rs3131968 A G -0.0054 0.019 0.7800007 0.7762490 NA 0.6785140 9635.32
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51212875 rs2238837 A C -0.0067 0.0120 0.5600000 0.5766168 0.3917 0.3724040 21167.60
22 51213613 rs34726907 C T 0.0450 0.0350 0.2000000 0.1985428 NA 0.1727240 1873.00
22 51219006 rs28729663 G A 0.0400 0.0320 0.2099999 0.2112995 NA 0.2052720 2004.00
22 51229805 rs9616985 T C 0.0009 0.0250 0.9699999 0.9712824 0.0917 0.0730831 18125.90
23 35921591 rs2204667 C G 0.0085 0.0090 0.3500000 0.3449426 0.1667 NA 34596.60
23 51666786 rs14115 A G 0.0005 0.0150 0.9699999 0.9734088 0.0250 NA 34577.10
23 70163799 rs1626496 A C 0.0190 0.0130 0.1499999 0.1438677 0.0750 NA 34646.10
23 91415872 rs6562597 G A -0.0140 0.0240 0.5500004 0.5596689 0.0083 0.0021192 33017.50
23 117417363 rs11541996 G A 0.2800 0.2900 0.3400001 0.3342858 NA NA 611.92
23 118495837 rs12882977 G A 0.0057 0.0068 0.4000000 0.4018986 0.5167 0.2307280 34599.80

bcf preview

1   564477  rs6650104   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.003:0.19:0.00436481:611.86:rs6650104
1   721290  rs12565286  G   C   .   PASS    .   ES:SE:LP:SS:ID  0.02:0.027:0.337242:16035:rs12565286
1   723819  rs11804171  T   A   .   PASS    .   ES:SE:LP:SS:ID  0.017:0.027:0.275724:16049:rs11804171
1   723891  rs2977670   G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.01:0.038:0.107905:7582.97:rs2977670
1   740857  rs12082473  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.32:0.24:0.744727:2008:rs12082473
1   752566  rs3094315   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0035:0.011:0.124939:24211:rs3094315
1   752721  rs3131972   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.062:0.061:0.508638:611.91:rs3131972
1   753541  rs2073813   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.014:0.016:0.443698:15809:rs2073813
1   754182  rs3131969   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.051:0.063:0.376751:611.97:rs3131969
1   754192  rs3131968   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.0054:0.019:0.107905:9635.32:rs3131968
1   760912  rs1048488   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.051:0.061:0.39794:611.97:rs1048488
1   761732  rs2286139   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.023:0.03:0.356547:1989:rs2286139
1   768448  rs12562034  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.0083:0.023:0.142668:13223.4:rs12562034
1   775659  rs2905035   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.011:0.012:0.443698:25443.6:rs2905035
1   776546  rs12124819  A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.034:0.021:0.958607:4759.41:rs12124819
1   777122  rs2980319   A   T   .   PASS    .   ES:SE:LP:SS:ID  -0.011:0.012:0.420216:25444.6:rs2980319
1   779322  rs4040617   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.0093:0.012:0.356547:25385.1:rs4040617
1   780785  rs2977612   T   A   .   PASS    .   ES:SE:LP:SS:ID  -0.011:0.012:0.420216:25443.5:rs2977612
1   784023  rs17160939  A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.088:0.089:0.49485:2857.98:rs17160939
1   785050  rs2905062   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.011:0.012:0.443698:25443.4:rs2905062
1   785989  rs2980300   T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.012:0.012:0.49485:25389.5:rs2980300
1   798026  rs4951864   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.002:0.022:0.0315171:13223.3:rs4951864
1   798801  rs12132517  G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.0093:0.022:0.173925:13222.8:rs12132517
1   798959  rs11240777  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.011:0.014:0.356547:15246.9:rs11240777
1   838555  rs4970383   C   A   .   PASS    .   ES:SE:LP:SS:ID  -0.089:0.056:0.958607:611.99:rs4970383
1   846808  rs4475691   C   T   .   PASS    .   ES:SE:LP:SS:ID  0.0025:0.027:0.0315171:3051.98:rs4475691
1   846864  rs950122    G   C   .   PASS    .   ES:SE:LP:SS:ID  -0.052:0.1:0.207608:611.24:rs950122
1   854250  rs7537756   A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.097:0.062:0.920819:612:rs7537756
1   861808  rs13302982  A   G   .   PASS    .   ES:SE:LP:SS:ID  -0.13:0.13:0.481486:612:rs13302982
1   863124  rs4040604   G   T   .   PASS    .   ES:SE:LP:SS:ID  -0.16:0.13:0.619789:612:rs4040604
1   870645  rs28576697  T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.0061:0.054:0.0409586:611.94:rs28576697
1   879317  rs7523549   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.14:0.14:0.508638:611.93:rs7523549
1   880238  rs3748592   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.057:0.11:0.229148:612:rs3748592
1   880390  rs3748593   C   A   .   PASS    .   ES:SE:LP:SS:ID  -0.24:0.15:0.920819:612:rs3748593
1   882033  rs2272756   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.013:0.056:0.0861861:612:rs2272756
1   882803  rs2340582   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.034:0.11:0.124939:611.99:rs2340582
1   884815  rs4246503   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.057:0.11:0.229148:612:rs4246503
1   886384  rs3748594   G   A   .   PASS    .   ES:SE:LP:SS:ID  -0.21:0.15:0.79588:612:rs3748594
1   887560  rs3748595   A   C   .   PASS    .   ES:SE:LP:SS:ID  0.034:0.099:0.136677:611.98:rs3748595
1   888659  rs3748597   T   C   .   PASS    .   ES:SE:LP:SS:ID  -0.024:0.049:0.200659:2199:rs3748597
1   891945  rs13303106  A   G   .   PASS    .   ES:SE:LP:SS:ID  0.048:0.051:0.455932:611.99:rs13303106
1   893981  rs28415373  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.91:0.36:1.95861:611.99:rs28415373
1   894573  rs13303010  G   A   .   PASS    .   ES:SE:LP:SS:ID  0.081:0.085:0.468521:611.99:rs13303010
1   900505  rs28705211  G   C   .   PASS    .   ES:SE:LP:SS:ID  0.024:0.023:0.522879:3051.73:rs28705211
1   900730  rs3935066   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.092:0.088:0.522879:612:rs3935066
1   903104  rs6696281   C   T   .   PASS    .   ES:SE:LP:SS:ID  -0.13:0.13:0.49485:612:rs6696281
1   908414  rs28504611  C   T   .   PASS    .   ES:SE:LP:SS:ID  0.19:0.14:0.769551:611.95:rs28504611
1   916834  rs6694632   G   A   .   PASS    .   ES:SE:LP:SS:ID  0.016:0.049:0.130768:612:rs6694632
1   918384  rs13303118  G   T   .   PASS    .   ES:SE:LP:SS:ID  -0.0064:0.021:0.119186:2857.07:rs13303118
1   918573  rs2341354   A   G   .   PASS    .   ES:SE:LP:SS:ID  0.046:0.023:1.3279:2018:rs2341354