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/public/UKB-b-16376/UKB-b-16376_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16376/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:00 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16376/UKB-b-16376_data.vcf.gz ...
Read summary statistics for 7225482 SNPs.
Dropped 4669 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, 1270037 SNPs remain.
After merging with regression SNP LD, 1270037 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0158 (0.0014)
Lambda GC: 1.1627
Mean Chi^2: 1.181
Intercept: 1.038 (0.0077)
Ratio: 0.21 (0.0423)
Analysis finished at Thu Oct 17 14:46:19 2019
Total time elapsed: 1.0m:19.22s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9377,
    "inflation_factor": 1.1474,
    "mean_EFFECT": 4.0871e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 8,
    "n_p_sig": 404,
    "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": 66703,
    "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": 1270037,
    "ldsc_nsnp_merge_regression_ld": 1270037,
    "ldsc_observed_scale_h2_beta": 0.0158,
    "ldsc_observed_scale_h2_se": 0.0014,
    "ldsc_intercept_beta": 1.038,
    "ldsc_intercept_se": 0.0077,
    "ldsc_lambda_gc": 1.1627,
    "ldsc_mean_chisq": 1.181,
    "ldsc_ratio": 0.2099
}

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 7220835 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 7225482 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 8.663405e+00 5.763513e+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 7.863163e+07 5.645339e+07 828.0000000 3.215545e+07 6.905442e+07 1.145122e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 4.100000e-06 9.888000e-04 -0.0081649 -5.193000e-04 2.700000e-06 5.243000e-04 1.926620e-02 ▁▇▁▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 8.447000e-04 4.196000e-04 0.0004489 5.096000e-04 6.667000e-04 1.072400e-03 4.868300e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.785429e-01 2.940671e-01 0.0000000 2.200002e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.785435e-01 2.940416e-01 0.0000000 2.186266e-01 4.705135e-01 7.333549e-01 9.999999e-01 ▇▇▇▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.685667e-01 2.603638e-01 0.0142570 5.335000e-02 1.692570e-01 4.225758e-01 9.857430e-01 ▇▂▂▁▁
numeric AF_reference 66703 0.9907684 NA NA NA NA NA NA NA 2.671215e-01 2.522747e-01 0.0000000 6.010380e-02 1.819090e-01 4.157350e-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 0.0005560 0.0008259 0.5000000 0.5007938 0.623775 0.7821490 NA
1 54676 rs2462492 C T -0.0005023 0.0008183 0.5400003 0.5393074 0.400403 NA NA
1 86028 rs114608975 T C 0.0007067 0.0013083 0.5900000 0.5890481 0.103556 0.0277556 NA
1 91536 rs6702460 G T 0.0005838 0.0008056 0.4700002 0.4686887 0.456856 0.4207270 NA
1 234313 rs8179466 C T 0.0011101 0.0015882 0.4799997 0.4845716 0.074516 NA NA
1 534192 rs6680723 C T 0.0013959 0.0009203 0.1299999 0.1293009 0.240935 NA NA
1 546697 rs12025928 A G 0.0000242 0.0011480 0.9800000 0.9831915 0.913470 NA NA
1 693731 rs12238997 A G -0.0000884 0.0007713 0.9100000 0.9087055 0.116316 0.1417730 NA
1 705882 rs72631875 G A -0.0011109 0.0011302 0.3300000 0.3256106 0.067291 0.0315495 NA
1 706368 rs55727773 A G -0.0004147 0.0005713 0.4700002 0.4678899 0.515674 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0012505 0.0006892 0.0700003 0.0696386 0.137952 0.2052720 NA
22 51219387 rs9616832 T C 0.0013855 0.0008946 0.1199999 0.1214595 0.073736 0.0654952 NA
22 51219704 rs147475742 G A 0.0011371 0.0011987 0.3400001 0.3428136 0.041960 0.0473243 NA
22 51221190 rs369304721 G A 0.0026753 0.0011968 0.0250000 0.0253896 0.049733 NA NA
22 51221731 rs115055839 T C 0.0014189 0.0008952 0.1100001 0.1129489 0.073226 0.0625000 NA
22 51222100 rs114553188 G T 0.0008015 0.0010539 0.4500005 0.4469527 0.054469 0.0880591 NA
22 51223637 rs375798137 G A 0.0007008 0.0010590 0.5099998 0.5081402 0.054098 0.0788738 NA
22 51229805 rs9616985 T C 0.0014080 0.0008984 0.1199999 0.1170706 0.073063 0.0730831 NA
22 51232488 rs376461333 A G -0.0005007 0.0021168 0.8100000 0.8130190 0.020043 NA NA
22 51237063 rs3896457 T C 0.0011536 0.0005496 0.0359998 0.0358092 0.297985 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623775 ES:SE:LP:AF:ID  0.000555999:0.000825854:0.30103:0.623775:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400403 ES:SE:LP:AF:ID  -0.000502316:0.00081829:0.267606:0.400403:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103556 ES:SE:LP:AF:ID  0.000706743:0.00130826:0.229148:0.103556:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456856 ES:SE:LP:AF:ID  0.000583755:0.000805608:0.327902:0.456856:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074516 ES:SE:LP:AF:ID  0.00111013:0.00158824:0.318759:0.074516:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240935 ES:SE:LP:AF:ID  0.00139594:0.00092028:0.886057:0.240935:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91347  ES:SE:LP:AF:ID  2.41866e-05:0.00114803:0.00877392:0.91347:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116316 ES:SE:LP:AF:ID  -8.84435e-05:0.000771277:0.0409586:0.116316:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067291 ES:SE:LP:AF:ID  -0.00111094:0.00113016:0.481486:0.067291:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515674 ES:SE:LP:AF:ID  -0.000414727:0.000571315:0.327902:0.515674:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033015 ES:SE:LP:AF:ID  0.000601637:0.00143998:0.167491:0.033015:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036629 ES:SE:LP:AF:ID  0.000656903:0.00130807:0.207608:0.036629:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036745 ES:SE:LP:AF:ID  0.000658375:0.00130311:0.21467:0.036745:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036444 ES:SE:LP:AF:ID  0.000682335:0.00131253:0.221849:0.036444:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016411 ES:SE:LP:AF:ID  0.00437248:0.00202086:1.52288:0.016411:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036982 ES:SE:LP:AF:ID  0.000715651:0.001298:0.236572:0.036982:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037079 ES:SE:LP:AF:ID  0.000776335:0.00129353:0.259637:0.037079:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101203 ES:SE:LP:AF:ID  -0.00033807:0.00094261:0.142668:0.101203:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959084 ES:SE:LP:AF:ID  -0.000822203:0.00124751:0.29243:0.959084:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031433 ES:SE:LP:AF:ID  -0.00135872:0.00226661:0.259637:0.031433:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053268 ES:SE:LP:AF:ID  -0.00459917:0.00180125:1.95861:0.053268:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036597 ES:SE:LP:AF:ID  0.000716787:0.00130193:0.236572:0.036597:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036913 ES:SE:LP:AF:ID  0.000610542:0.00129008:0.19382:0.036913:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843201 ES:SE:LP:AF:ID  -1.53568e-05:0.000668367:0.00877392:0.843201:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05591  ES:SE:LP:AF:ID  -0.000987146:0.00108224:0.443698:0.05591:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122307 ES:SE:LP:AF:ID  -0.000277459:0.00073162:0.154902:0.122307:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025724 ES:SE:LP:AF:ID  0.00409958:0.00179905:1.63827:0.025724:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121549 ES:SE:LP:AF:ID  -0.00017488:0.000731926:0.091515:0.121549:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132333 ES:SE:LP:AF:ID  -0.000551702:0.000721386:0.356547:0.132333:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.036827 ES:SE:LP:AF:ID  0.000478221:0.00127703:0.148742:0.036827:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838939 ES:SE:LP:AF:ID  -7.122e-05:0.000647262:0.0409586:0.838939:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838566 ES:SE:LP:AF:ID  -6.34498e-05:0.000646563:0.0362122:0.838566:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869776 ES:SE:LP:AF:ID  0.000193713:0.0006938:0.107905:0.869776:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129875 ES:SE:LP:AF:ID  -0.000276815:0.000695223:0.161151:0.129875:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03734  ES:SE:LP:AF:ID  0.000376802:0.00125535:0.119186:0.03734:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037584 ES:SE:LP:AF:ID  0.000305131:0.00124742:0.091515:0.037584:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869116 ES:SE:LP:AF:ID  0.000194433:0.000692437:0.107905:0.869116:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869214 ES:SE:LP:AF:ID  0.000184616:0.000692709:0.102373:0.869214:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037543 ES:SE:LP:AF:ID  0.000361882:0.0012528:0.113509:0.037543:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86912  ES:SE:LP:AF:ID  0.000189673:0.000692424:0.107905:0.86912:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838019 ES:SE:LP:AF:ID  -2.89459e-05:0.000644768:0.0177288:0.838019:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037555 ES:SE:LP:AF:ID  0.000418515:0.00125458:0.130768:0.037555:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838649 ES:SE:LP:AF:ID  -8.46783e-05:0.000646579:0.0457575:0.838649:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839763 ES:SE:LP:AF:ID  -1.92773e-05:0.000655323:0.00877392:0.839763:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869401 ES:SE:LP:AF:ID  0.00018862:0.000691627:0.102373:0.869401:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.86895  ES:SE:LP:AF:ID  0.000133548:0.000689892:0.0705811:0.86895:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.8679   ES:SE:LP:AF:ID  3.58221e-05:0.000688562:0.0177288:0.8679:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869092 ES:SE:LP:AF:ID  0.000113632:0.000690454:0.0604807:0.869092:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869101 ES:SE:LP:AF:ID  0.000111472:0.000690507:0.0604807:0.869101:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869109 ES:SE:LP:AF:ID  0.000117366:0.000690523:0.0604807:0.869109:rs3131956