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_3456.vcf.gz --id UKB-b:469 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3456.txt.gz --cohort_controls 33229 --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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    "bcftools_viewCommand.1": "view -h /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukb-b-469/ukb-b-469.vcf.gz; Date=Sat May  9 15:03:13 2020"
}
 

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-469/UKB-b-469_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-469/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:33 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-469/UKB-b-469_data.vcf.gz ...
Read summary statistics for 7661249 SNPs.
Dropped 5473 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, 1278812 SNPs remain.
After merging with regression SNP LD, 1278812 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1013 (0.0187)
Lambda GC: 1.0679
Mean Chi^2: 1.0824
Intercept: 1.0165 (0.0064)
Ratio: 0.1996 (0.0782)
Analysis finished at Thu Oct 17 14:45:57 2019
Total time elapsed: 1.0m:23.96s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9411,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -3.5192e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 3,
    "n_p_sig": 515,
    "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": 71254,
    "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": 1278812,
    "ldsc_nsnp_merge_regression_ld": 1278812,
    "ldsc_observed_scale_h2_beta": 0.1013,
    "ldsc_observed_scale_h2_se": 0.0187,
    "ldsc_intercept_beta": 1.0165,
    "ldsc_intercept_se": 0.0064,
    "ldsc_lambda_gc": 1.0679,
    "ldsc_mean_chisq": 1.0824,
    "ldsc_ratio": 0.2002
}
 

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 7655800 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 7661249 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 8.662186e+00 5.764545e+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 7.867461e+07 5.644001e+07 828.0000000 3.219806e+07 6.912070e+07 1.145650e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -3.500000e-06 1.254480e-02 -0.1584460 -6.128000e-03 4.200000e-05 6.164500e-03 1.130360e-01 ▁▁▇▃▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.074280e-02 5.975900e-03 0.0052132 6.015400e-03 8.145100e-03 1.392790e-02 6.442210e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.909147e-01 2.914991e-01 0.0000000 2.399999e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.909145e-01 2.914750e-01 0.0000000 2.358570e-01 4.877858e-01 7.436486e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.552566e-01 2.608209e-01 0.0105330 4.245400e-02 1.504200e-01 4.033220e-01 9.894670e-01 ▇▂▂▁▁
numeric AF_reference 71254 0.9906994 NA NA NA NA NA NA NA 2.542718e-01 2.526881e-01 0.0000000 4.672520e-02 1.647360e-01 3.977640e-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 0.0162309 0.0096422 0.0920005 0.0923116 0.621965 0.7821490 NA
1 54676 rs2462492 C T -0.0120729 0.0095519 0.2099999 0.2062545 0.399383 NA NA
1 86028 rs114608975 T C -0.0273849 0.0153163 0.0739997 0.0737828 0.103548 0.0277556 NA
1 91536 rs6702460 G T 0.0118545 0.0094406 0.2099999 0.2092271 0.455872 0.4207270 NA
1 234313 rs8179466 C T 0.0169785 0.0187093 0.3599996 0.3641478 0.073901 NA NA
1 534192 rs6680723 C T -0.0028883 0.0106515 0.7899998 0.7862666 0.243684 NA NA
1 546697 rs12025928 A G -0.0004154 0.0136223 0.9800000 0.9756723 0.914901 NA NA
1 693731 rs12238997 A G -0.0024326 0.0089810 0.7899998 0.7865008 0.116426 0.1417730 NA
1 705882 rs72631875 G A -0.0049884 0.0133549 0.7099994 0.7087588 0.066144 0.0315495 NA
1 706368 rs55727773 A G -0.0061677 0.0066763 0.3599996 0.3555754 0.512625 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0085199 0.0081294 0.2900000 0.2946234 0.135538 0.2052720 NA
22 51219387 rs9616832 T C -0.0089367 0.0105444 0.4000000 0.3967016 0.072069 0.0654952 NA
22 51219704 rs147475742 G A -0.0163964 0.0141515 0.2500000 0.2466057 0.040874 0.0473243 NA
22 51221190 rs369304721 G A -0.0077189 0.0141411 0.5900000 0.5851695 0.048395 NA NA
22 51221731 rs115055839 T C -0.0097420 0.0105496 0.3599996 0.3557727 0.071665 0.0625000 NA
22 51222100 rs114553188 G T -0.0177886 0.0124358 0.1499999 0.1525923 0.053767 0.0880591 NA
22 51223637 rs375798137 G A -0.0181580 0.0124920 0.1499999 0.1460655 0.053383 0.0788738 NA
22 51229805 rs9616985 T C -0.0092566 0.0105756 0.3800004 0.3814196 0.071565 0.0730831 NA
22 51232488 rs376461333 A G -0.0286906 0.0254283 0.2599998 0.2591957 0.019267 NA NA
22 51237063 rs3896457 T C -0.0046015 0.0064129 0.4700002 0.4730452 0.297410 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.621965 ES:SE:LP:AF:ID  0.0162309:0.00964215:1.03621:0.621965:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399383 ES:SE:LP:AF:ID  -0.0120729:0.00955186:0.677781:0.399383:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103548 ES:SE:LP:AF:ID  -0.0273849:0.0153163:1.13077:0.103548:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455872 ES:SE:LP:AF:ID  0.0118545:0.0094406:0.677781:0.455872:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.073901 ES:SE:LP:AF:ID  0.0169785:0.0187093:0.443698:0.073901:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.243684 ES:SE:LP:AF:ID  -0.00288828:0.0106515:0.102373:0.243684:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.914901 ES:SE:LP:AF:ID  -0.000415412:0.0136223:0.00877392:0.914901:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116426 ES:SE:LP:AF:ID  -0.00243258:0.00898104:0.102373:0.116426:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.066144 ES:SE:LP:AF:ID  -0.00498837:0.0133549:0.148742:0.066144:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.512625 ES:SE:LP:AF:ID  -0.00616774:0.00667628:0.443698:0.512625:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033173 ES:SE:LP:AF:ID  -0.0200498:0.0167816:0.638272:0.033173:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036818 ES:SE:LP:AF:ID  -0.015428:0.0152271:0.508638:0.036818:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036884 ES:SE:LP:AF:ID  -0.0165101:0.0151843:0.552842:0.036884:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036612 ES:SE:LP:AF:ID  -0.0144768:0.015281:0.468521:0.036612:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01675  ES:SE:LP:AF:ID  -0.013303:0.0232758:0.244125:0.01675:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037171 ES:SE:LP:AF:ID  -0.015625:0.0151103:0.522879:0.037171:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037292 ES:SE:LP:AF:ID  -0.0145528:0.0150456:0.481486:0.037292:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.099773 ES:SE:LP:AF:ID  0.0133189:0.0110788:0.638272:0.099773:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958695 ES:SE:LP:AF:ID  0.0145416:0.0144521:0.508638:0.958695:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.030993 ES:SE:LP:AF:ID  0.0142831:0.0268868:0.221849:0.030993:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053407 ES:SE:LP:AF:ID  0.0202992:0.021072:0.468521:0.053407:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036897 ES:SE:LP:AF:ID  -0.0172412:0.0151355:0.60206:0.036897:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037107 ES:SE:LP:AF:ID  -0.0169849:0.0150227:0.585027:0.037107:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842358 ES:SE:LP:AF:ID  0.00854247:0.00775121:0.568636:0.842358:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.057114 ES:SE:LP:AF:ID  0.000442776:0.0125266:0.0132283:0.057114:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123087 ES:SE:LP:AF:ID  -0.00226843:0.00849288:0.102373:0.123087:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025709 ES:SE:LP:AF:ID  0.0023165:0.021185:0.0409586:0.025709:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122272 ES:SE:LP:AF:ID  -0.00211399:0.00849831:0.09691:0.122272:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132473 ES:SE:LP:AF:ID  -0.0093147:0.00840357:0.568636:0.132473:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011635 ES:SE:LP:AF:ID  -0.0454068:0.030097:0.886057:0.011635:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.03713  ES:SE:LP:AF:ID  -0.0165055:0.0148509:0.568636:0.03713:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838592 ES:SE:LP:AF:ID  0.0100528:0.0075292:0.744727:0.838592:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838174 ES:SE:LP:AF:ID  0.0105891:0.00751742:0.79588:0.838174:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869349 ES:SE:LP:AF:ID  0.00414506:0.0080788:0.21467:0.869349:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130614 ES:SE:LP:AF:ID  -0.00573291:0.00808477:0.318759:0.130614:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037562 ES:SE:LP:AF:ID  -0.0163964:0.0146137:0.585027:0.037562:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037831 ES:SE:LP:AF:ID  -0.0153492:0.0145217:0.537602:0.037831:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868711 ES:SE:LP:AF:ID  0.00476243:0.00806106:0.259637:0.868711:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868775 ES:SE:LP:AF:ID  0.00453996:0.00806275:0.244125:0.868775:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037785 ES:SE:LP:AF:ID  -0.0158333:0.0145756:0.552842:0.037785:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868725 ES:SE:LP:AF:ID  0.00479421:0.00806075:0.259637:0.868725:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.83767  ES:SE:LP:AF:ID  0.0108884:0.00750124:0.823909:0.83767:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037811 ES:SE:LP:AF:ID  -0.0157459:0.0145947:0.552842:0.037811:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838328 ES:SE:LP:AF:ID  0.0100965:0.0075232:0.744727:0.838328:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013848 ES:SE:LP:AF:ID  0.00949362:0.0263755:0.142668:0.013848:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.839512 ES:SE:LP:AF:ID  0.00933606:0.00762614:0.657577:0.839512:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868978 ES:SE:LP:AF:ID  0.00400436:0.00805242:0.207608:0.868978:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868496 ES:SE:LP:AF:ID  0.00344377:0.00803299:0.173925:0.868496:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867414 ES:SE:LP:AF:ID  0.0050091:0.00801762:0.275724:0.867414:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868717 ES:SE:LP:AF:ID  0.00384551:0.00804109:0.200659:0.868717:rs4951929