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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}

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/ukb-d-import/processed/ukb-d-4803_13/ukb-d-4803_13.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/ukb-d-import/processed/ukb-d-4803_13/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Mon Nov 25 16:33:14 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-4803_13/ukb-d-4803_13.vcf.gz ...
Read summary statistics for 13352736 SNPs.
Dropped 12394 SNPs with duplicated rs numbers.
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, 1283467 SNPs remain.
After merging with regression SNP LD, 1283467 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0114 (0.0037)
Lambda GC: 1.0279
Mean Chi^2: 1.0213
Intercept: 0.9947 (0.0058)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 16:35:31 2019
Total time elapsed: 2.0m:17.39s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9518,
    "inflation_factor": 1.0175,
    "mean_EFFECT": 6.6744e-06,
    "n": 117882,
    "n_snps": 13352736,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 1243873,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 507669,
    "n_est": 118364.6307,
    "ratio_se_n": 1.002,
    "mean_diff": 0,
    "ratio_diff": 17.0842,
    "sd_y_est1": 0.2855,
    "sd_y_est2": 0.2861,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1283467,
    "ldsc_nsnp_merge_regression_ld": 1283467,
    "ldsc_observed_scale_h2_beta": 0.0114,
    "ldsc_observed_scale_h2_se": 0.0037,
    "ldsc_intercept_beta": 0.9947,
    "ldsc_intercept_se": 0.0058,
    "ldsc_lambda_gc": 1.0279,
    "ldsc_mean_chisq": 1.0213,
    "ldsc_ratio": -0.2488
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n FALSE
is_snpid_non_unique TRUE
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 94 0 13341002 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 56663 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33212 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.054331e+00 6.185590e+00 1.00000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA 7.900124e+07 5.592543e+07 3.02000e+02 3.288750e+07 7.009583e+07 1.148315e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 6.700000e-06 6.485900e-03 -6.89477e-02 -1.810200e-03 -1.200000e-05 1.774000e-03 9.000930e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 4.703000e-03 4.448900e-03 9.66000e-04 1.398600e-03 2.517900e-03 6.724900e-03 2.398640e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.973775e-01 2.890758e-01 3.00000e-07 2.464976e-01 4.962745e-01 7.478302e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.973762e-01 2.890766e-01 3.00000e-07 2.464951e-01 4.962743e-01 7.478304e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.939720e-01 2.551733e-01 1.19170e-03 8.268700e-03 6.398810e-02 3.016670e-01 9.988080e-01 ▇▂▁▁▁
numeric AF_reference 507669 0.9619802 NA NA NA NA NA 1.986947e-01 2.476212e-01 0.00000e+00 6.190100e-03 8.746010e-02 3.127000e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 1.178820e+05 0.000000e+00 1.17882e+05 1.178820e+05 1.178820e+05 1.178820e+05 1.178820e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0000836 0.0020562 0.9675749 0.9675744 0.1115810 0.1894970 117882
1 693731 rs12238997 A G 0.0000373 0.0019447 0.9847050 0.9847047 0.1168770 0.1417730 117882
1 707522 rs371890604 G C 0.0006464 0.0021832 0.7671654 0.7671643 0.0981926 0.1293930 117882
1 717587 rs144155419 G A 0.0015445 0.0052364 0.7680244 0.7680241 0.0157291 0.0045926 117882
1 723329 rs189787166 A T -0.0241620 0.0155485 0.1201921 0.1201901 0.0016918 0.0003994 117882
1 730087 rs148120343 T C -0.0013183 0.0027148 0.6272534 0.6272525 0.0567623 0.0127796 117882
1 731718 rs142557973 T C 0.0000600 0.0018464 0.9740681 0.9740681 0.1227030 0.1543530 117882
1 732032 rs61770163 A C 0.0002204 0.0019678 0.9108230 0.9108223 0.1222200 0.1555510 117882
1 734349 rs141242758 T C -0.0000539 0.0018476 0.9767070 0.9767073 0.1219220 0.1525560 117882
1 740284 rs61770167 C T -0.0082805 0.0082523 0.3156640 0.3156613 0.0060284 0.0023962 117882
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0023719 0.0021537 0.2707572 0.2707556 0.0549643 0.0309934 117882
23 154923374 rs111332691 T A 0.0010752 0.0023523 0.6476241 0.6476236 0.0442010 0.0116556 117882
23 154925045 rs509981 C T -0.0002264 0.0011290 0.8410819 0.8410816 0.2443250 0.3634440 117882
23 154925895 rs538470 C T -0.0003842 0.0011543 0.7392716 0.7392703 0.2408360 0.3634440 117882
23 154927581 rs644138 G A -0.0007885 0.0010620 0.4577897 0.4577862 0.2996770 0.4635760 117882
23 154929412 rs557132 C T -0.0002362 0.0011295 0.8343729 0.8343717 0.2441460 0.3568210 117882
23 154929637 rs35185538 CT C 0.0006053 0.0011783 0.6074716 0.6074696 0.2280540 0.3011920 117882
23 154929952 rs4012982 CAA C -0.0006945 0.0011886 0.5590363 0.5590358 0.2381380 0.3165560 117882
23 154930230 rs781880 A G -0.0002998 0.0011292 0.7905912 0.7905915 0.2446240 0.3618540 117882
23 154930487 rs781879 T A 0.0045283 0.0039321 0.2494721 0.2494701 0.0190093 0.1263580 117882

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111581 ES:SE:LP:AF:SS:ID   8.35876e-05:0.00205624:0.0143154:0.111581:117882:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116877 ES:SE:LP:AF:SS:ID   3.72814e-05:0.00194468:0.00669386:0.116877:117882:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0981926    ES:SE:LP:AF:SS:ID   0.000646424:0.00218323:0.115111:0.0981926:117882:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157291    ES:SE:LP:AF:SS:ID   0.00154454:0.00523643:0.114625:0.0157291:117882:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00169177   ES:SE:LP:AF:SS:ID   -0.024162:0.0155485:0.920124:0.00169177:117882:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0567623    ES:SE:LP:AF:SS:ID   -0.00131831:0.00271482:0.202557:0.0567623:117882:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122703 ES:SE:LP:AF:SS:ID   6.002e-05:0.0018464:0.0114107:0.122703:117882:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.12222  ES:SE:LP:AF:SS:ID   0.000220394:0.00196778:0.040566:0.12222:117882:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121922 ES:SE:LP:AF:SS:ID   -5.39444e-05:0.00184759:0.0102357:0.121922:117882:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00602836   ES:SE:LP:AF:SS:ID   -0.00828047:0.0082523:0.500775:0.00602836:117882:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00178226   ES:SE:LP:AF:SS:ID   -0.000353095:0.0166031:0.0074319:0.00178226:117882:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.868783 ES:SE:LP:AF:SS:ID   -0.000666767:0.0018216:0.146094:0.868783:117882:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00160222   ES:SE:LP:AF:SS:ID   0.0075279:0.0160515:0.194444:0.00160222:117882:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.12379  ES:SE:LP:AF:SS:ID   0.000928366:0.00180443:0.216876:0.12379:117882:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143426 ES:SE:LP:AF:SS:ID   0.00157991:0.0017869:0.424109:0.143426:117882:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.12391  ES:SE:LP:AF:SS:ID   0.00104398:0.00180179:0.250023:0.12391:117882:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869962 ES:SE:LP:AF:SS:ID   -0.00130054:0.00175491:0.338526:0.869962:117882:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874645 ES:SE:LP:AF:SS:ID   -0.00147745:0.00178446:0.389661:0.874645:117882:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129609 ES:SE:LP:AF:SS:ID   0.00103636:0.00175868:0.25518:0.129609:117882:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0368674    ES:SE:LP:AF:SS:ID   -0.00617681:0.00317073:1.28896:0.0368674:117882:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86957  ES:SE:LP:AF:SS:ID   -0.00117587:0.00175312:0.298956:0.86957:117882:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869693 ES:SE:LP:AF:SS:ID   -0.00122236:0.00175409:0.313461:0.869693:117882:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.86956  ES:SE:LP:AF:SS:ID   -0.00117291:0.00175301:0.298048:0.86956:117882:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00506018   ES:SE:LP:AF:SS:ID   0.018944:0.00901959:1.44729:0.00506018:117882:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00502696   ES:SE:LP:AF:SS:ID   0.0186009:0.0090437:1.40111:0.00502696:117882:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00551475   ES:SE:LP:AF:SS:ID   -0.00155846:0.0087561:0.0661413:0.00551475:117882:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.869687 ES:SE:LP:AF:SS:ID   -0.00127003:0.00175016:0.32971:0.869687:117882:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126898 ES:SE:LP:AF:SS:ID   0.00108741:0.00178582:0.265533:0.126898:117882:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.869268 ES:SE:LP:AF:SS:ID   -0.00115171:0.00174612:0.292838:0.869268:117882:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868472 ES:SE:LP:AF:SS:ID   -0.000939062:0.00174397:0.228957:0.868472:117882:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869416 ES:SE:LP:AF:SS:ID   -0.00115815:0.00174752:0.294563:0.869416:117882:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869423 ES:SE:LP:AF:SS:ID   -0.00115866:0.00174765:0.294698:0.869423:117882:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869431 ES:SE:LP:AF:SS:ID   -0.00116583:0.00174771:0.296939:0.869431:117882:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869858 ES:SE:LP:AF:SS:ID   -0.00129107:0.00175217:0.336093:0.869858:117882:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0997694    ES:SE:LP:AF:SS:ID   0.000474796:0.00203198:0.0887103:0.0997694:117882:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00632595   ES:SE:LP:AF:SS:ID   -0.000702854:0.00794379:0.031752:0.00632595:117882:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873792 ES:SE:LP:AF:SS:ID   -0.00161151:0.00177894:0.437707:0.873792:117882:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863113 ES:SE:LP:AF:SS:ID   -0.000725285:0.00174426:0.16906:0.863113:117882:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868567 ES:SE:LP:AF:SS:ID   -0.0012973:0.00176113:0.335972:0.868567:117882:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865357 ES:SE:LP:AF:SS:ID   -0.00114022:0.001761:0.28624:0.865357:117882:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0994301    ES:SE:LP:AF:SS:ID   0.00105455:0.00211166:0.209362:0.0994301:117882:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870598 ES:SE:LP:AF:SS:ID   -0.00136408:0.00178126:0.352814:0.870598:117882:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870598 ES:SE:LP:AF:SS:ID   -0.00136447:0.00178126:0.352941:0.870598:117882:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.8706   ES:SE:LP:AF:SS:ID   -0.00136842:0.00178135:0.35421:0.8706:117882:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.870994 ES:SE:LP:AF:SS:ID   -0.00136331:0.00178246:0.352263:0.870994:117882:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126474 ES:SE:LP:AF:SS:ID   0.00116531:0.00178354:0.289443:0.126474:117882:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106227 ES:SE:LP:AF:SS:ID   0.000239202:0.00194029:0.0448488:0.106227:117882:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854511 ES:SE:LP:AF:SS:ID   -0.00113668:0.00174438:0.288494:0.854511:117882:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00159212   ES:SE:LP:AF:SS:ID   -0.024632:0.0156659:0.935999:0.00159212:117882:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.837943 ES:SE:LP:AF:SS:ID   -0.0021944:0.00171627:0.696707:0.837943:117882:rs376645387