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-18336/UKB-b-18336_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18336/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18336/UKB-b-18336_data.vcf.gz ...
Read summary statistics for 8335802 SNPs.
Dropped 6793 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, 1284569 SNPs remain.
After merging with regression SNP LD, 1284569 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0299 (0.0018)
Lambda GC: 1.2316
Mean Chi^2: 1.284
Intercept: 1.0184 (0.0089)
Ratio: 0.0648 (0.0312)
Analysis finished at Thu Oct 17 14:41:51 2019
Total time elapsed: 1.0m:33.06s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9448,
    "inflation_factor": 1.1999,
    "mean_EFFECT": 8.6022e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 6,
    "n_p_sig": 2692,
    "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": 78529,
    "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": 1284569,
    "ldsc_nsnp_merge_regression_ld": 1284569,
    "ldsc_observed_scale_h2_beta": 0.0299,
    "ldsc_observed_scale_h2_se": 0.0018,
    "ldsc_intercept_beta": 1.0184,
    "ldsc_intercept_se": 0.0089,
    "ldsc_lambda_gc": 1.2316,
    "ldsc_mean_chisq": 1.284,
    "ldsc_ratio": 0.0648
}

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 TRUE
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 8329039 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 8335802 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 8.654590e+00 5.762193e+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 7.874082e+07 5.638495e+07 828.0000000 3.233551e+07 6.923587e+07 1.145491e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 8.600000e-06 1.905800e-03 -0.0192323 -8.619000e-04 2.400000e-06 8.755000e-04 1.822580e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.519300e-03 9.813000e-04 0.0006426 7.512000e-04 1.078000e-03 2.028900e-03 1.005540e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.717594e-01 2.968038e-01 0.0000000 2.099999e-01 4.600002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.717611e-01 2.967795e-01 0.0000000 2.075107e-01 4.622422e-01 7.290567e-01 9.999999e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.364562e-01 2.601770e-01 0.0065530 2.957400e-02 1.240910e-01 3.744670e-01 9.934470e-01 ▇▂▂▁▁
numeric AF_reference 78529 0.9905793 NA NA NA NA NA NA NA 2.359739e-01 2.520741e-01 0.0000000 2.915340e-02 1.399760e-01 3.708070e-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 -0.0011233 0.0011825 0.3400001 0.3421275 0.623790 0.7821490 NA
1 54676 rs2462492 C T -0.0012812 0.0011714 0.2700001 0.2741027 0.400383 NA NA
1 86028 rs114608975 T C 0.0027372 0.0018729 0.1400000 0.1438932 0.103553 0.0277556 NA
1 91536 rs6702460 G T 0.0008317 0.0011535 0.4700002 0.4708613 0.456819 0.4207270 NA
1 234313 rs8179466 C T 0.0001472 0.0022745 0.9500000 0.9483943 0.074498 NA NA
1 534192 rs6680723 C T 0.0011615 0.0013175 0.3800004 0.3780129 0.240953 NA NA
1 546697 rs12025928 A G 0.0014920 0.0016437 0.3599996 0.3640359 0.913490 NA NA
1 693731 rs12238997 A G 0.0000990 0.0011041 0.9299999 0.9285211 0.116333 0.1417730 NA
1 705882 rs72631875 G A 0.0000683 0.0016183 0.9699999 0.9663248 0.067261 0.0315495 NA
1 706368 rs55727773 A G -0.0006715 0.0008179 0.4100001 0.4116510 0.515684 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0005566 0.0009868 0.5700002 0.5727572 0.137968 0.2052720 NA
22 51219387 rs9616832 T C -0.0007064 0.0012810 0.5800000 0.5813632 0.073744 0.0654952 NA
22 51219704 rs147475742 G A -0.0014634 0.0017165 0.3900004 0.3939183 0.041961 0.0473243 NA
22 51221190 rs369304721 G A -0.0004416 0.0017138 0.8000000 0.7966673 0.049733 NA NA
22 51221731 rs115055839 T C -0.0007245 0.0012819 0.5700002 0.5719639 0.073233 0.0625000 NA
22 51222100 rs114553188 G T 0.0000204 0.0015089 0.9900000 0.9892013 0.054470 0.0880591 NA
22 51223637 rs375798137 G A -0.0002091 0.0015162 0.8900000 0.8902895 0.054098 0.0788738 NA
22 51229805 rs9616985 T C -0.0006341 0.0012865 0.6200004 0.6221057 0.073069 0.0730831 NA
22 51232488 rs376461333 A G -0.0002592 0.0030308 0.9299999 0.9318529 0.020042 NA NA
22 51237063 rs3896457 T C -0.0004185 0.0007869 0.5900000 0.5948347 0.297929 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62379  ES:SE:LP:AF:ID  -0.00112333:0.00118249:0.468521:0.62379:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400383 ES:SE:LP:AF:ID  -0.00128115:0.00117143:0.568636:0.400383:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103553 ES:SE:LP:AF:ID  0.0027372:0.00187294:0.853872:0.103553:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456819 ES:SE:LP:AF:ID  0.000831735:0.00115346:0.327902:0.456819:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074498 ES:SE:LP:AF:ID  0.000147212:0.00227448:0.0222764:0.074498:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240953 ES:SE:LP:AF:ID  0.00116146:0.0013175:0.420216:0.240953:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91349  ES:SE:LP:AF:ID  0.00149198:0.00164369:0.443698:0.91349:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116333 ES:SE:LP:AF:ID  9.90404e-05:0.00110406:0.0315171:0.116333:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067261 ES:SE:LP:AF:ID  6.83212e-05:0.00161829:0.0132283:0.067261:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515684 ES:SE:LP:AF:ID  -0.000671503:0.000817917:0.387216:0.515684:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032993 ES:SE:LP:AF:ID  0.000557787:0.00206226:0.102373:0.032993:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036612 ES:SE:LP:AF:ID  0.000643957:0.00187309:0.136677:0.036612:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036725 ES:SE:LP:AF:ID  0.000729904:0.0018661:0.154902:0.036725:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036422 ES:SE:LP:AF:ID  0.000607127:0.0018796:0.124939:0.036422:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016404 ES:SE:LP:AF:ID  -0.00191834:0.00289368:0.29243:0.016404:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036964 ES:SE:LP:AF:ID  0.000832253:0.0018587:0.187087:0.036964:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037059 ES:SE:LP:AF:ID  0.000601444:0.00185239:0.124939:0.037059:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101207 ES:SE:LP:AF:ID  -0.00254927:0.00134941:1.22915:0.101207:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959108 ES:SE:LP:AF:ID  -0.000747251:0.00178658:0.167491:0.959108:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03145  ES:SE:LP:AF:ID  0.000974912:0.0032427:0.119186:0.03145:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053259 ES:SE:LP:AF:ID  -0.00232333:0.00257959:0.431798:0.053259:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036577 ES:SE:LP:AF:ID  0.000954351:0.00186438:0.21467:0.036577:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036894 ES:SE:LP:AF:ID  0.000740748:0.00184738:0.161151:0.036894:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.84322  ES:SE:LP:AF:ID  -0.000503539:0.000956872:0.221849:0.84322:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05591  ES:SE:LP:AF:ID  0.00146506:0.00154945:0.468521:0.05591:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122311 ES:SE:LP:AF:ID  0.000266863:0.00104737:0.09691:0.122311:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025718 ES:SE:LP:AF:ID  -0.000880032:0.00257641:0.136677:0.025718:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121555 ES:SE:LP:AF:ID  0.000245586:0.00104781:0.091515:0.121555:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13233  ES:SE:LP:AF:ID  0.000626595:0.00103267:0.267606:0.13233:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011132 ES:SE:LP:AF:ID  0.00301684:0.00375499:0.376751:0.011132:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.036807 ES:SE:LP:AF:ID  0.000684803:0.00182877:0.148742:0.036807:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838956 ES:SE:LP:AF:ID  -0.000511105:0.000926662:0.236572:0.838956:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838584 ES:SE:LP:AF:ID  -0.000507652:0.000925662:0.236572:0.838584:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869777 ES:SE:LP:AF:ID  -0.000334836:0.000993315:0.130768:0.869777:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129871 ES:SE:LP:AF:ID  0.000360773:0.000995345:0.142668:0.129871:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037316 ES:SE:LP:AF:ID  0.000766717:0.00179779:0.173925:0.037316:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037561 ES:SE:LP:AF:ID  0.000834755:0.00178641:0.19382:0.037561:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86912  ES:SE:LP:AF:ID  -0.0003225:0.000991367:0.130768:0.86912:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869218 ES:SE:LP:AF:ID  -0.000301839:0.000991763:0.119186:0.869218:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037519 ES:SE:LP:AF:ID  0.000761921:0.00179414:0.173925:0.037519:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869122 ES:SE:LP:AF:ID  -0.000320356:0.000991348:0.124939:0.869122:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838039 ES:SE:LP:AF:ID  -0.000453139:0.000923106:0.207608:0.838039:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037532 ES:SE:LP:AF:ID  0.000654116:0.00179667:0.142668:0.037532:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.83867  ES:SE:LP:AF:ID  -0.000457516:0.000925704:0.207608:0.83867:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013784 ES:SE:LP:AF:ID  0.00123611:0.00322959:0.154902:0.013784:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.839781 ES:SE:LP:AF:ID  -0.000583469:0.000938219:0.275724:0.839781:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869399 ES:SE:LP:AF:ID  -0.000228128:0.00099019:0.0861861:0.869399:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868948 ES:SE:LP:AF:ID  -0.000250343:0.000987702:0.09691:0.868948:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867902 ES:SE:LP:AF:ID  -0.000223128:0.000985802:0.0861861:0.867902:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.86909  ES:SE:LP:AF:ID  -0.000205681:0.000988508:0.0757207:0.86909:rs4951929