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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    "FORMAT.1": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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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_20006.vcf.gz --id UKB-b:8800 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20006.txt.gz --cohort_controls 39107 --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-8800/UKB-b-8800_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8800/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:05 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8800/UKB-b-8800_data.vcf.gz ...
Read summary statistics for 7888771 SNPs.
Dropped 5918 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, 1281161 SNPs remain.
After merging with regression SNP LD, 1281161 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0148 (0.0112)
Lambda GC: 1.0136
Mean Chi^2: 1.0124
Intercept: 1.001 (0.006)
Ratio: 0.0838 (0.4788)
Analysis finished at Thu Oct 17 14:43:33 2019
Total time elapsed: 1.0m:28.21s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9422,
    "inflation_factor": 1,
    "mean_EFFECT": 7.852e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 8,
    "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": 73590,
    "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": 1281161,
    "ldsc_nsnp_merge_regression_ld": 1281161,
    "ldsc_observed_scale_h2_beta": 0.0148,
    "ldsc_observed_scale_h2_se": 0.0112,
    "ldsc_intercept_beta": 1.001,
    "ldsc_intercept_se": 0.006,
    "ldsc_lambda_gc": 1.0136,
    "ldsc_mean_chisq": 1.0124,
    "ldsc_ratio": 0.0806
}

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 7882879 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 7888771 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 8.661500e+00 5.764024e+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 7.869720e+07 5.642338e+07 828.0000000 3.224216e+07 6.915434e+07 1.145541e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 7.900000e-06 1.693970e-02 -0.1700920 -7.951400e-03 -2.450000e-05 7.940500e-03 1.638230e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.457360e-02 8.558200e-03 0.0067738 7.829800e-03 1.080420e-02 1.909030e-02 8.302180e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.976757e-01 2.893196e-01 0.0000000 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 4.976768e-01 2.892928e-01 0.0000000 2.468643e-01 4.964865e-01 7.482091e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.486221e-01 2.608166e-01 0.0089500 3.750650e-02 1.408360e-01 3.935840e-01 9.910500e-01 ▇▂▂▁▁
numeric AF_reference 73590 0.9906716 NA NA NA NA NA NA NA 2.477909e-01 2.526394e-01 0.0000000 4.013580e-02 1.559500e-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 0.0038616 0.0124384 0.7600007 0.7562109 0.623864 0.7821490 NA
1 54676 rs2462492 C T 0.0001867 0.0123440 0.9900000 0.9879347 0.401269 NA NA
1 86028 rs114608975 T C -0.0152116 0.0198565 0.4400003 0.4436307 0.103769 0.0277556 NA
1 91536 rs6702460 G T 0.0181487 0.0121142 0.1299999 0.1340983 0.457194 0.4207270 NA
1 234313 rs8179466 C T -0.0007341 0.0238815 0.9800000 0.9754781 0.074944 NA NA
1 534192 rs6680723 C T -0.0236744 0.0139245 0.0890000 0.0890938 0.240322 NA NA
1 546697 rs12025928 A G 0.0240200 0.0172292 0.1600000 0.1632739 0.912727 NA NA
1 693731 rs12238997 A G -0.0216446 0.0115894 0.0619998 0.0618150 0.117715 0.1417730 NA
1 705882 rs72631875 G A -0.0206319 0.0169734 0.2200002 0.2241590 0.067556 0.0315495 NA
1 706368 rs55727773 A G 0.0136722 0.0086007 0.1100001 0.1119099 0.515197 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0088551 0.0104621 0.4000000 0.3973308 0.136437 0.2052720 NA
22 51219387 rs9616832 T C 0.0051987 0.0135245 0.6999999 0.7006868 0.073167 0.0654952 NA
22 51219704 rs147475742 G A 0.0069541 0.0182405 0.6999999 0.7030198 0.041328 0.0473243 NA
22 51221190 rs369304721 G A 0.0160894 0.0181857 0.3800004 0.3763033 0.049128 NA NA
22 51221731 rs115055839 T C 0.0058486 0.0135317 0.6700003 0.6655843 0.072691 0.0625000 NA
22 51222100 rs114553188 G T -0.0330367 0.0159786 0.0389996 0.0386815 0.053969 0.0880591 NA
22 51223637 rs375798137 G A -0.0343061 0.0160629 0.0329997 0.0327010 0.053585 0.0788738 NA
22 51229805 rs9616985 T C 0.0078090 0.0135741 0.5700002 0.5650989 0.072617 0.0730831 NA
22 51232488 rs376461333 A G -0.0042724 0.0322683 0.8900000 0.8946673 0.019646 NA NA
22 51237063 rs3896457 T C -0.0031506 0.0083090 0.6999999 0.7045569 0.298436 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623864 ES:SE:LP:AF:ID  0.00386163:0.0124384:0.119186:0.623864:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.401269 ES:SE:LP:AF:ID  0.000186668:0.012344:0.00436481:0.401269:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103769 ES:SE:LP:AF:ID  -0.0152116:0.0198565:0.356547:0.103769:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457194 ES:SE:LP:AF:ID  0.0181487:0.0121142:0.886057:0.457194:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074944 ES:SE:LP:AF:ID  -0.000734082:0.0238815:0.00877392:0.074944:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240322 ES:SE:LP:AF:ID  -0.0236744:0.0139245:1.05061:0.240322:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912727 ES:SE:LP:AF:ID  0.02402:0.0172292:0.79588:0.912727:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117715 ES:SE:LP:AF:ID  -0.0216446:0.0115894:1.20761:0.117715:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067556 ES:SE:LP:AF:ID  -0.0206319:0.0169734:0.657577:0.067556:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515197 ES:SE:LP:AF:ID  0.0136722:0.00860067:0.958607:0.515197:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.031796 ES:SE:LP:AF:ID  -0.032894:0.0221176:0.853872:0.031796:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.035444 ES:SE:LP:AF:ID  -0.0261447:0.020041:0.721246:0.035444:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.035587 ES:SE:LP:AF:ID  -0.0255688:0.0199607:0.69897:0.035587:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.035267 ES:SE:LP:AF:ID  -0.0246235:0.0201228:0.657577:0.035267:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016344 ES:SE:LP:AF:ID  -0.00209069:0.0305279:0.0222764:0.016344:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.035745 ES:SE:LP:AF:ID  -0.0275542:0.0199122:0.769551:0.035745:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.035836 ES:SE:LP:AF:ID  -0.0273831:0.0198407:0.769551:0.035836:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.100085 ES:SE:LP:AF:ID  0.0108272:0.0143609:0.346787:0.100085:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.960492 ES:SE:LP:AF:ID  0.0179479:0.0191609:0.455932:0.960492:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03146  ES:SE:LP:AF:ID  0.00129099:0.0339713:0.0132283:0.03146:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053723 ES:SE:LP:AF:ID  0.0285555:0.0266654:0.552842:0.053723:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.035393 ES:SE:LP:AF:ID  -0.0269709:0.0199606:0.744727:0.035393:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.035735 ES:SE:LP:AF:ID  -0.024535:0.0197594:0.677781:0.035735:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843117 ES:SE:LP:AF:ID  0.020485:0.0100963:1.37675:0.843117:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056509 ES:SE:LP:AF:ID  -0.0172303:0.0162824:0.537602:0.056509:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123697 ES:SE:LP:AF:ID  -0.0200656:0.0110032:1.16749:0.123697:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.02571  ES:SE:LP:AF:ID  -0.0173828:0.0272175:0.283997:0.02571:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122957 ES:SE:LP:AF:ID  -0.0190066:0.0110074:1.07572:0.122957:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132748 ES:SE:LP:AF:ID  -0.0261567:0.0108747:1.79588:0.132748:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011256 ES:SE:LP:AF:ID  0.0158987:0.0390199:0.167491:0.011256:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.035602 ES:SE:LP:AF:ID  -0.0238554:0.0195764:0.657577:0.035602:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838835 ES:SE:LP:AF:ID  0.0211236:0.00977599:1.50864:0.838835:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838546 ES:SE:LP:AF:ID  0.020124:0.00976983:1.40894:0.838546:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868626 ES:SE:LP:AF:ID  0.0162086:0.0104443:0.920819:0.868626:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131035 ES:SE:LP:AF:ID  -0.0158:0.0104605:0.886057:0.131035:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.036008 ES:SE:LP:AF:ID  -0.0235271:0.0192828:0.657577:0.036008:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.036221 ES:SE:LP:AF:ID  -0.0215268:0.0191692:0.585027:0.036221:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868002 ES:SE:LP:AF:ID  0.0152736:0.0104252:0.853872:0.868002:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868121 ES:SE:LP:AF:ID  0.0149722:0.0104285:0.823909:0.868121:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.036176 ES:SE:LP:AF:ID  -0.0219297:0.0192469:0.60206:0.036176:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868011 ES:SE:LP:AF:ID  0.0154781:0.0104262:0.853872:0.868011:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838071 ES:SE:LP:AF:ID  0.0195528:0.00974336:1.34679:0.838071:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.03619  ES:SE:LP:AF:ID  -0.0223028:0.01927:0.60206:0.03619:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838691 ES:SE:LP:AF:ID  0.0193833:0.00977083:1.3279:0.838691:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01349  ES:SE:LP:AF:ID  0.0459861:0.0344876:0.744727:0.01349:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.839552 ES:SE:LP:AF:ID  0.0203313:0.00988711:1.39794:0.839552:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868268 ES:SE:LP:AF:ID  0.0154979:0.0104125:0.853872:0.868268:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.867837 ES:SE:LP:AF:ID  0.0159401:0.0103879:0.920819:0.867837:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.866734 ES:SE:LP:AF:ID  0.015298:0.0103675:0.853872:0.866734:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.867925 ES:SE:LP:AF:ID  0.0161289:0.0103941:0.920819:0.867925:rs4951929