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/public/UKB-b-6392/UKB-b-6392_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6392/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6392/UKB-b-6392_data.vcf.gz ...
Read summary statistics for 7967398 SNPs.
Dropped 6079 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../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 ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1282202 SNPs remain.
After merging with regression SNP LD, 1282202 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0357 (0.0102)
Lambda GC: 1.0321
Mean Chi^2: 1.0239
Intercept: 0.9951 (0.0057)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:41:50 2019
Total time elapsed: 1.0m:31.27s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9429,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 0,
    "n_miss_AF_reference": 74402,
    "n_est": "NA",
    "ratio_se_n": "NA",
    "mean_diff": "NaN",
    "ratio_diff": "NaN",
    "sd_y_est1": "NaN",
    "sd_y_est2": "NA",
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1282202,
    "ldsc_nsnp_merge_regression_ld": 1282202,
    "ldsc_observed_scale_h2_beta": 0.0357,
    "ldsc_observed_scale_h2_se": 0.0102,
    "ldsc_intercept_beta": 0.9951,
    "ldsc_intercept_se": 0.0057,
    "ldsc_lambda_gc": 1.0321,
    "ldsc_mean_chisq": 1.0239,
    "ldsc_ratio": -0.205
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n TRUE
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 logical.mean logical.count numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 58 0 7961346 0 NA NA 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 NA NA
character ALT 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 7967398 0.0000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA NA NA 8.660507e+00 5.763553e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.869706e+07 5.641226e+07 828.0000000 3.225922e+07 6.916170e+07 1.145349e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -3.000000e-05 1.701460e-02 -0.1767010 -7.907800e-03 1.810000e-05 7.917500e-03 1.556620e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.452140e-02 8.673600e-03 0.0066314 7.694600e-03 1.068630e-02 1.908360e-02 7.938390e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.953315e-01 2.896725e-01 0.0000001 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.953338e-01 2.896466e-01 0.0000002 2.428681e-01 4.929060e-01 7.462942e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.464533e-01 2.607283e-01 0.0084990 3.603800e-02 1.378330e-01 3.901630e-01 9.915010e-01 ▇▂▂▁▁
numeric AF_reference 74402 0.9906617 NA NA NA NA NA NA NA 2.457205e-01 2.525827e-01 0.0000000 3.813900e-02 1.531550e-01 3.855830e-01 1.000000e+00 ▇▃▂▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 49298 rs200943160 T C 0.0212718 0.0121502 0.0800000 0.0799913 0.621223 0.7821490 NA
1 54676 rs2462492 C T -0.0134539 0.0121292 0.2700001 0.2673371 0.400841 NA NA
1 86028 rs114608975 T C -0.0114182 0.0189971 0.5500004 0.5478069 0.105363 0.0277556 NA
1 91536 rs6702460 G T -0.0006936 0.0119810 0.9500000 0.9538330 0.454955 0.4207270 NA
1 234313 rs8179466 C T 0.0281053 0.0240060 0.2399999 0.2416947 0.073643 NA NA
1 534192 rs6680723 C T 0.0027501 0.0135681 0.8400000 0.8393790 0.243140 NA NA
1 546697 rs12025928 A G -0.0196908 0.0170135 0.2500000 0.2471240 0.913578 NA NA
1 693731 rs12238997 A G -0.0067541 0.0114517 0.5600000 0.5553313 0.117651 0.1417730 NA
1 705882 rs72631875 G A -0.0053919 0.0166296 0.7499995 0.7457582 0.067851 0.0315495 NA
1 706368 rs55727773 A G 0.0018847 0.0084365 0.8200001 0.8232284 0.514038 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0115107 0.0102953 0.2599998 0.2635440 0.136645 0.2052720 NA
22 51219387 rs9616832 T C -0.0240678 0.0133752 0.0719996 0.0719499 0.072080 0.0654952 NA
22 51219704 rs147475742 G A -0.0490069 0.0178801 0.0061000 0.0061278 0.040875 0.0473243 NA
22 51221190 rs369304721 G A -0.0207398 0.0179190 0.2500000 0.2471010 0.048579 NA NA
22 51221731 rs115055839 T C -0.0252772 0.0133855 0.0589997 0.0589721 0.071584 0.0625000 NA
22 51222100 rs114553188 G T 0.0015070 0.0155006 0.9199999 0.9225501 0.054932 0.0880591 NA
22 51223637 rs375798137 G A 0.0017564 0.0155982 0.9100000 0.9103460 0.054456 0.0788738 NA
22 51229805 rs9616985 T C -0.0265989 0.0134340 0.0479999 0.0477070 0.071449 0.0730831 NA
22 51232488 rs376461333 A G 0.0279927 0.0312521 0.3700002 0.3704097 0.020297 NA NA
22 51237063 rs3896457 T C 0.0055104 0.0081792 0.5000000 0.5004959 0.297492 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.621223 ES:SE:LP:AF:ID  0.0212718:0.0121502:1.09691:0.621223:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400841 ES:SE:LP:AF:ID  -0.0134539:0.0121292:0.568636:0.400841:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.105363 ES:SE:LP:AF:ID  -0.0114182:0.0189971:0.259637:0.105363:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.454955 ES:SE:LP:AF:ID  -0.000693629:0.011981:0.0222764:0.454955:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.073643 ES:SE:LP:AF:ID  0.0281053:0.024006:0.619789:0.073643:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24314  ES:SE:LP:AF:ID  0.00275009:0.0135681:0.0757207:0.24314:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913578 ES:SE:LP:AF:ID  -0.0196908:0.0170135:0.60206:0.913578:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117651 ES:SE:LP:AF:ID  -0.0067541:0.0114517:0.251812:0.117651:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067851 ES:SE:LP:AF:ID  -0.00539194:0.0166296:0.124939:0.067851:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514038 ES:SE:LP:AF:ID  0.00188466:0.00843646:0.0861861:0.514038:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033627 ES:SE:LP:AF:ID  -0.0222876:0.0211009:0.537602:0.033627:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.03718  ES:SE:LP:AF:ID  -0.0208716:0.0192252:0.552842:0.03718:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037335 ES:SE:LP:AF:ID  -0.0212736:0.0191419:0.568636:0.037335:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036916 ES:SE:LP:AF:ID  -0.023714:0.019315:0.657577:0.036916:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016201 ES:SE:LP:AF:ID  0.0490239:0.0301674:1:0.016201:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03757  ES:SE:LP:AF:ID  -0.0209378:0.0190672:0.568636:0.03757:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03767  ES:SE:LP:AF:ID  -0.0221544:0.0190074:0.619789:0.03767:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101365 ES:SE:LP:AF:ID  2.9694e-05:0.0138971:-0:0.101365:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958006 ES:SE:LP:AF:ID  0.0232101:0.0182287:0.69897:0.958006:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031624 ES:SE:LP:AF:ID  0.00976506:0.0333569:0.113509:0.031624:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052436 ES:SE:LP:AF:ID  0.0277953:0.0270511:0.522879:0.052436:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037174 ES:SE:LP:AF:ID  -0.0195813:0.0191278:0.508638:0.037174:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037495 ES:SE:LP:AF:ID  -0.0166101:0.0189523:0.420216:0.037495:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.840718 ES:SE:LP:AF:ID  0.00575398:0.00989317:0.251812:0.840718:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.057284 ES:SE:LP:AF:ID  -0.0182851:0.015918:0.60206:0.057284:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.1239   ES:SE:LP:AF:ID  0.00125025:0.0108561:0.0409586:0.1239:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.026319 ES:SE:LP:AF:ID  0.0399168:0.026313:0.886057:0.026319:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.123072 ES:SE:LP:AF:ID  -0.000259551:0.010864:0.00877392:0.123072:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134059 ES:SE:LP:AF:ID  -0.0154284:0.0106874:0.823909:0.134059:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.010826 ES:SE:LP:AF:ID  -0.0823823:0.0398288:1.40894:0.010826:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.037296 ES:SE:LP:AF:ID  -0.0225154:0.0188159:0.638272:0.037296:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836331 ES:SE:LP:AF:ID  0.00648933:0.00959579:0.30103:0.836331:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.835932 ES:SE:LP:AF:ID  0.00741837:0.00958612:0.356547:0.835932:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.867495 ES:SE:LP:AF:ID  -0.000397277:0.0102714:0.0132283:0.867495:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.132165 ES:SE:LP:AF:ID  9.19004e-05:0.0103011:0.00436481:0.132165:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037811 ES:SE:LP:AF:ID  -0.0224115:0.0184904:0.638272:0.037811:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038064 ES:SE:LP:AF:ID  -0.0221684:0.0183729:0.638272:0.038064:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.866806 ES:SE:LP:AF:ID  0.000331967:0.0102539:0.0132283:0.866806:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86692  ES:SE:LP:AF:ID  0.000460108:0.010258:0.0177288:0.86692:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038034 ES:SE:LP:AF:ID  -0.0232315:0.0184387:0.677781:0.038034:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.866823 ES:SE:LP:AF:ID  0.000444336:0.0102534:0.0132283:0.866823:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.835376 ES:SE:LP:AF:ID  0.00719288:0.00956235:0.346787:0.835376:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038052 ES:SE:LP:AF:ID  -0.0220901:0.0184605:0.638272:0.038052:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.835992 ES:SE:LP:AF:ID  0.00666018:0.0095896:0.309804:0.835992:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013115 ES:SE:LP:AF:ID  0.0155132:0.0343437:0.187087:0.013115:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.836875 ES:SE:LP:AF:ID  0.00495258:0.00970558:0.21467:0.836875:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.867092 ES:SE:LP:AF:ID  -0.000517542:0.0102408:0.0177288:0.867092:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.86671  ES:SE:LP:AF:ID  -0.00151285:0.0102204:0.0555173:0.86671:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.865497 ES:SE:LP:AF:ID  -0.000300096:0.0101973:0.00877392:0.865497:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.866801 ES:SE:LP:AF:ID  -0.0011271:0.0102256:0.0409586:0.866801:rs4951929