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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_3627.vcf.gz --id UKB-b:3825 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3627.txt.gz --cohort_controls 13806 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
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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-3825/UKB-b-3825_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3825/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:47 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3825/UKB-b-3825_data.vcf.gz ...
Read summary statistics for 6408540 SNPs.
Dropped 3387 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, 1238846 SNPs remain.
After merging with regression SNP LD, 1238846 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.053 (0.041)
Lambda GC: 1.021
Mean Chi^2: 1.026
Intercept: 1.0113 (0.0077)
Ratio: 0.434 (0.2961)
Analysis finished at Thu Oct 17 14:45:01 2019
Total time elapsed: 1.0m:13.67s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9297,
    "inflation_factor": 1,
    "mean_EFFECT": -0.0003,
    "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": 58758,
    "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": 1238846,
    "ldsc_nsnp_merge_regression_ld": 1238846,
    "ldsc_observed_scale_h2_beta": 0.053,
    "ldsc_observed_scale_h2_se": 0.041,
    "ldsc_intercept_beta": 1.0113,
    "ldsc_intercept_se": 0.0077,
    "ldsc_lambda_gc": 1.021,
    "ldsc_mean_chisq": 1.026,
    "ldsc_ratio": 0.4346
}

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 6405174 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 6408540 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 8.667962e+00 5.763404e+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 7.856997e+07 5.648341e+07 828.0000000 3.202637e+07 6.901246e+07 1.144555e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -2.522000e-04 2.019580e-02 -0.1904960 -1.175050e-02 -1.662000e-04 1.133320e-02 1.646930e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.869360e-02 7.240800e-03 0.0115000 1.279660e-02 1.577940e-02 2.281600e-02 9.421960e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.970005e-01 2.896368e-01 0.0000002 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.969998e-01 2.896117e-01 0.0000002 2.452436e-01 4.962886e-01 7.477560e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.964377e-01 2.568224e-01 0.0253520 8.007100e-02 2.083680e-01 4.587900e-01 9.746470e-01 ▇▃▂▂▁
numeric AF_reference 58758 0.9908313 NA NA NA NA NA NA NA 2.937567e-01 2.493146e-01 0.0000000 8.925720e-02 2.176520e-01 4.504790e-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 0.0335753 0.0211622 0.1100001 0.1126102 0.622779 0.7821490 NA
1 54676 rs2462492 C T -0.0287168 0.0211486 0.1700000 0.1745087 0.398105 NA NA
1 86028 rs114608975 T C 0.0536785 0.0338569 0.1100001 0.1128636 0.103296 0.0277556 NA
1 91536 rs6702460 G T -0.0057019 0.0204738 0.7800007 0.7806309 0.457089 0.4207270 NA
1 234313 rs8179466 C T -0.0690947 0.0402238 0.0860003 0.0858410 0.074293 NA NA
1 534192 rs6680723 C T 0.0033664 0.0236119 0.8900000 0.8866267 0.239147 NA NA
1 546697 rs12025928 A G -0.0050644 0.0296862 0.8600001 0.8645406 0.914819 NA NA
1 693731 rs12238997 A G 0.0065845 0.0201164 0.7400005 0.7434270 0.111635 0.1417730 NA
1 705882 rs72631875 G A -0.0176243 0.0289295 0.5400003 0.5423816 0.066378 0.0315495 NA
1 706368 rs55727773 A G 0.0103028 0.0145734 0.4799997 0.4795918 0.517776 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C -0.0186577 0.0228992 0.4199997 0.4152010 0.073770 0.0826677 NA
22 51219006 rs28729663 G A -0.0151021 0.0176005 0.3900004 0.3908651 0.139032 0.2052720 NA
22 51219387 rs9616832 T C -0.0189284 0.0229155 0.4100001 0.4087992 0.074024 0.0654952 NA
22 51219704 rs147475742 G A -0.0023882 0.0300656 0.9400001 0.9366869 0.043740 0.0473243 NA
22 51221190 rs369304721 G A -0.0065508 0.0306762 0.8300000 0.8309003 0.050344 NA NA
22 51221731 rs115055839 T C -0.0196170 0.0229264 0.3900004 0.3921908 0.073551 0.0625000 NA
22 51222100 rs114553188 G T -0.0078146 0.0269129 0.7700005 0.7715369 0.054853 0.0880591 NA
22 51223637 rs375798137 G A -0.0053303 0.0270434 0.8400000 0.8437474 0.054458 0.0788738 NA
22 51229805 rs9616985 T C -0.0200679 0.0229804 0.3800004 0.3825205 0.073360 0.0730831 NA
22 51237063 rs3896457 T C -0.0006922 0.0141130 0.9599999 0.9608808 0.303409 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.622779 ES:SE:LP:AF:ID  0.0335753:0.0211622:0.958607:0.622779:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.398105 ES:SE:LP:AF:ID  -0.0287168:0.0211486:0.769551:0.398105:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103296 ES:SE:LP:AF:ID  0.0536785:0.0338569:0.958607:0.103296:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457089 ES:SE:LP:AF:ID  -0.00570189:0.0204738:0.107905:0.457089:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074293 ES:SE:LP:AF:ID  -0.0690947:0.0402238:1.0655:0.074293:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.239147 ES:SE:LP:AF:ID  0.00336644:0.0236119:0.05061:0.239147:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.914819 ES:SE:LP:AF:ID  -0.00506438:0.0296862:0.0655015:0.914819:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.111635 ES:SE:LP:AF:ID  0.00658447:0.0201164:0.130768:0.111635:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.066378 ES:SE:LP:AF:ID  -0.0176243:0.0289295:0.267606:0.066378:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.517776 ES:SE:LP:AF:ID  0.0103028:0.0145734:0.318759:0.517776:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033133 ES:SE:LP:AF:ID  -0.0249626:0.036786:0.30103:0.033133:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036999 ES:SE:LP:AF:ID  -0.0187257:0.0332779:0.244125:0.036999:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.0372   ES:SE:LP:AF:ID  -0.0207538:0.033146:0.275724:0.0372:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036659 ES:SE:LP:AF:ID  -0.0150836:0.0335122:0.187087:0.036659:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.037291 ES:SE:LP:AF:ID  -0.020051:0.0330932:0.267606:0.037291:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037359 ES:SE:LP:AF:ID  -0.0185972:0.0329782:0.244125:0.037359:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102038 ES:SE:LP:AF:ID  -0.0186069:0.0237895:0.366532:0.102038:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959032 ES:SE:LP:AF:ID  0.0171027:0.0319286:0.229148:0.959032:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031376 ES:SE:LP:AF:ID  -0.00276396:0.0579192:0.0177288:0.031376:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052929 ES:SE:LP:AF:ID  -0.011194:0.0464084:0.091515:0.052929:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036902 ES:SE:LP:AF:ID  -0.0200354:0.033156:0.259637:0.036902:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036984 ES:SE:LP:AF:ID  -0.0184388:0.0329832:0.236572:0.036984:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.84793  ES:SE:LP:AF:ID  0.00582652:0.0172592:0.130768:0.84793:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.053132 ES:SE:LP:AF:ID  0.0160598:0.0282937:0.244125:0.053132:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.11747  ES:SE:LP:AF:ID  -0.0011676:0.0190797:0.0222764:0.11747:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025764 ES:SE:LP:AF:ID  0.0548687:0.0460227:0.638272:0.025764:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.116827 ES:SE:LP:AF:ID  -0.00142486:0.0190762:0.0268721:0.116827:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.128181 ES:SE:LP:AF:ID  -0.0107447:0.0187191:0.244125:0.128181:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.036923 ES:SE:LP:AF:ID  -0.0287222:0.0326373:0.420216:0.036923:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.843952 ES:SE:LP:AF:ID  0.0126766:0.0167286:0.346787:0.843952:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.843655 ES:SE:LP:AF:ID  0.0126117:0.0167018:0.346787:0.843655:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.875174 ES:SE:LP:AF:ID  0.00453798:0.0180871:0.09691:0.875174:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.124406 ES:SE:LP:AF:ID  -0.00337345:0.0181173:0.0705811:0.124406:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037574 ES:SE:LP:AF:ID  -0.0226741:0.032036:0.318759:0.037574:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037846 ES:SE:LP:AF:ID  -0.0247377:0.0318212:0.356547:0.037846:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.874665 ES:SE:LP:AF:ID  0.00385671:0.0180439:0.0809219:0.874665:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.874702 ES:SE:LP:AF:ID  0.00378288:0.0180519:0.0809219:0.874702:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037754 ES:SE:LP:AF:ID  -0.0233115:0.0319824:0.327902:0.037754:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.874667 ES:SE:LP:AF:ID  0.0038671:0.018043:0.0809219:0.874667:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.8432   ES:SE:LP:AF:ID  0.0121049:0.0166703:0.327902:0.8432:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037779 ES:SE:LP:AF:ID  -0.0230275:0.0320255:0.327902:0.037779:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.843719 ES:SE:LP:AF:ID  0.0121881:0.0167151:0.327902:0.843719:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.844847 ES:SE:LP:AF:ID  0.015223:0.0169671:0.431798:0.844847:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.874836 ES:SE:LP:AF:ID  0.00633252:0.0180285:0.136677:0.874836:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.874294 ES:SE:LP:AF:ID  0.00831331:0.0179624:0.19382:0.874294:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.873246 ES:SE:LP:AF:ID  0.00562879:0.0179304:0.124939:0.873246:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.87452  ES:SE:LP:AF:ID  0.00663971:0.0179902:0.148742:0.87452:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.874527 ES:SE:LP:AF:ID  0.00660887:0.0179917:0.148742:0.874527:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.874527 ES:SE:LP:AF:ID  0.00665116:0.0179914:0.148742:0.874527:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.875063 ES:SE:LP:AF:ID  0.00632232:0.0180568:0.136677:0.875063:rs3131954