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-8633/UKB-b-8633_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8633/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:00 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8633/UKB-b-8633_data.vcf.gz ...
Read summary statistics for 7886630 SNPs.
Dropped 5911 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, 1281247 SNPs remain.
After merging with regression SNP LD, 1281247 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.009 (0.0011)
Lambda GC: 1.1366
Mean Chi^2: 1.1545
Intercept: 1.0751 (0.0066)
Ratio: 0.4862 (0.0424)
Analysis finished at Thu Oct 17 14:43:37 2019
Total time elapsed: 1.0m:37.31s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9423,
    "inflation_factor": 1.1474,
    "mean_EFFECT": 9.7173e-06,
    "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": 73545,
    "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": 1281247,
    "ldsc_nsnp_merge_regression_ld": 1281247,
    "ldsc_observed_scale_h2_beta": 0.009,
    "ldsc_observed_scale_h2_se": 0.0011,
    "ldsc_intercept_beta": 1.0751,
    "ldsc_intercept_se": 0.0066,
    "ldsc_lambda_gc": 1.1366,
    "ldsc_mean_chisq": 1.1545,
    "ldsc_ratio": 0.4861
}

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 7880746 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 7886630 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 8.661152e+00 5.763702e+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 7.870417e+07 5.642490e+07 828.0000000 3.224621e+07 6.917191e+07 1.145585e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 9.700000e-06 1.457500e-03 -0.0147548 -6.838000e-04 4.200000e-06 6.979000e-04 1.481870e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.199200e-03 7.036000e-04 0.0005583 6.447000e-04 8.892000e-04 1.570700e-03 6.878000e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.811047e-01 2.942526e-01 0.0000002 2.200002e-01 4.700002e-01 7.400005e-01 1.000000e+00 ▇▇▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.811037e-01 2.942269e-01 0.0000002 2.210478e-01 4.748937e-01 7.362843e-01 9.999997e-01 ▇▇▇▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.486973e-01 2.607983e-01 0.0089760 3.758400e-02 1.409880e-01 3.936710e-01 9.910240e-01 ▇▂▂▁▁
numeric AF_reference 73545 0.9906747 NA NA NA NA NA NA NA 2.478808e-01 2.526355e-01 0.0000000 4.033550e-02 1.561500e-01 3.885780e-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 -0.0014153 0.0010272 0.1700000 0.1682459 0.623776 0.7821490 NA
1 54676 rs2462492 C T 0.0011855 0.0010179 0.2399999 0.2441450 0.400406 NA NA
1 86028 rs114608975 T C -0.0008380 0.0016272 0.6100002 0.6065566 0.103552 0.0277556 NA
1 91536 rs6702460 G T 0.0005073 0.0010021 0.6100002 0.6126692 0.456866 0.4207270 NA
1 234313 rs8179466 C T 0.0011044 0.0019757 0.5800000 0.5761766 0.074512 NA NA
1 534192 rs6680723 C T 0.0012581 0.0011446 0.2700001 0.2717069 0.240954 NA NA
1 546697 rs12025928 A G 0.0003033 0.0014281 0.8300000 0.8317930 0.913483 NA NA
1 693731 rs12238997 A G 0.0004732 0.0009593 0.6200004 0.6218094 0.116319 0.1417730 NA
1 705882 rs72631875 G A -0.0012056 0.0014057 0.3900004 0.3910639 0.067296 0.0315495 NA
1 706368 rs55727773 A G -0.0006996 0.0007106 0.3200000 0.3248589 0.515686 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0018173 0.0008574 0.0340001 0.0340346 0.137947 0.2052720 NA
22 51219387 rs9616832 T C 0.0015637 0.0011128 0.1600000 0.1599893 0.073732 0.0654952 NA
22 51219704 rs147475742 G A 0.0021222 0.0014911 0.1499999 0.1546647 0.041961 0.0473243 NA
22 51221190 rs369304721 G A 0.0023562 0.0014888 0.1100001 0.1135119 0.049725 NA NA
22 51221731 rs115055839 T C 0.0013246 0.0011135 0.2300001 0.2342302 0.073223 0.0625000 NA
22 51222100 rs114553188 G T 0.0018456 0.0013109 0.1600000 0.1591820 0.054470 0.0880591 NA
22 51223637 rs375798137 G A 0.0018320 0.0013173 0.1600000 0.1642850 0.054099 0.0788738 NA
22 51229805 rs9616985 T C 0.0013383 0.0011176 0.2300001 0.2311139 0.073061 0.0730831 NA
22 51232488 rs376461333 A G 0.0059146 0.0026326 0.0250000 0.0246622 0.020044 NA NA
22 51237063 rs3896457 T C 0.0002069 0.0006836 0.7600007 0.7621705 0.297985 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623776 ES:SE:LP:AF:ID  -0.00141534:0.0010272:0.769551:0.623776:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400406 ES:SE:LP:AF:ID  0.00118549:0.00101786:0.619789:0.400406:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103552 ES:SE:LP:AF:ID  -0.00083801:0.00162722:0.21467:0.103552:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456866 ES:SE:LP:AF:ID  0.00050731:0.00100206:0.21467:0.456866:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074512 ES:SE:LP:AF:ID  0.00110439:0.00197573:0.236572:0.074512:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240954 ES:SE:LP:AF:ID  0.00125812:0.00114464:0.568636:0.240954:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913483 ES:SE:LP:AF:ID  0.00030333:0.00142809:0.0809219:0.913483:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116319 ES:SE:LP:AF:ID  0.000473198:0.000959274:0.207608:0.116319:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067296 ES:SE:LP:AF:ID  -0.00120562:0.00140566:0.408935:0.067296:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515686 ES:SE:LP:AF:ID  -0.00069962:0.000710619:0.49485:0.515686:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033005 ES:SE:LP:AF:ID  0.000628746:0.00179133:0.136677:0.033005:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.03662  ES:SE:LP:AF:ID  0.000691309:0.00162717:0.173925:0.03662:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036737 ES:SE:LP:AF:ID  0.000637095:0.001621:0.161151:0.036737:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036435 ES:SE:LP:AF:ID  0.000522592:0.00163271:0.124939:0.036435:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016413 ES:SE:LP:AF:ID  -0.00134505:0.00251343:0.229148:0.016413:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036973 ES:SE:LP:AF:ID  0.000696838:0.00161464:0.173925:0.036973:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03707  ES:SE:LP:AF:ID  0.00051806:0.00160909:0.124939:0.03707:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101208 ES:SE:LP:AF:ID  0.00111001:0.00117237:0.468521:0.101208:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959094 ES:SE:LP:AF:ID  -0.000125826:0.00155185:0.0268721:0.959094:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031434 ES:SE:LP:AF:ID  0.000256661:0.00281922:0.0315171:0.031434:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053266 ES:SE:LP:AF:ID  -0.00330761:0.00224063:0.853872:0.053266:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036588 ES:SE:LP:AF:ID  0.000745788:0.00161951:0.187087:0.036588:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036904 ES:SE:LP:AF:ID  0.000999573:0.00160478:0.275724:0.036904:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843207 ES:SE:LP:AF:ID  -0.000430703:0.000831322:0.221849:0.843207:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055906 ES:SE:LP:AF:ID  0.000665952:0.00134612:0.207608:0.055906:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122306 ES:SE:LP:AF:ID  3.28521e-05:0.000909968:0.0132283:0.122306:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025718 ES:SE:LP:AF:ID  0.00187008:0.00223804:0.39794:0.025718:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121548 ES:SE:LP:AF:ID  7.71788e-05:0.000910349:0.0315171:0.121548:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132327 ES:SE:LP:AF:ID  0.000644082:0.000897261:0.327902:0.132327:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011128 ES:SE:LP:AF:ID  0.00321415:0.0032631:0.49485:0.011128:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.036819 ES:SE:LP:AF:ID  0.000839202:0.00158855:0.221849:0.036819:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.83895  ES:SE:LP:AF:ID  -0.000598854:0.000805086:0.337242:0.83895:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838577 ES:SE:LP:AF:ID  -0.000593191:0.000804217:0.337242:0.838577:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869782 ES:SE:LP:AF:ID  -0.000251871:0.000862956:0.113509:0.869782:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.12987  ES:SE:LP:AF:ID  0.000246509:0.000864715:0.107905:0.12987:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037331 ES:SE:LP:AF:ID  0.000498014:0.00156158:0.124939:0.037331:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037574 ES:SE:LP:AF:ID  0.000487577:0.00155173:0.124939:0.037574:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869123 ES:SE:LP:AF:ID  -0.000220138:0.00086126:0.09691:0.869123:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869221 ES:SE:LP:AF:ID  -0.000173239:0.000861603:0.0757207:0.869221:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037533 ES:SE:LP:AF:ID  0.000414491:0.00155842:0.102373:0.037533:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869126 ES:SE:LP:AF:ID  -0.000237664:0.000861245:0.107905:0.869126:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838031 ES:SE:LP:AF:ID  -0.000540005:0.000801986:0.30103:0.838031:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037546 ES:SE:LP:AF:ID  0.000480245:0.00156063:0.119186:0.037546:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838661 ES:SE:LP:AF:ID  -0.000555963:0.00080424:0.309804:0.838661:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013773 ES:SE:LP:AF:ID  -0.000430153:0.00280731:0.0555173:0.013773:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.839776 ES:SE:LP:AF:ID  -0.000506045:0.000815117:0.275724:0.839776:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869407 ES:SE:LP:AF:ID  -0.000279057:0.000860256:0.124939:0.869407:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868957 ES:SE:LP:AF:ID  -0.000263679:0.0008581:0.119186:0.868957:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867907 ES:SE:LP:AF:ID  -0.000266512:0.000856446:0.119186:0.867907:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869099 ES:SE:LP:AF:ID  -0.000267222:0.000858798:0.119186:0.869099:rs4951929