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_22146.vcf.gz --id UKB-b:18290 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_22146.txt.gz --cohort_controls 25486 --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-18290/UKB-b-18290_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18290/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-18290/UKB-b-18290_data.vcf.gz ...
Read summary statistics for 7277268 SNPs.
Dropped 4742 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, 1271250 SNPs remain.
After merging with regression SNP LD, 1271250 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1115 (0.0195)
Lambda GC: 1.0561
Mean Chi^2: 1.063
Intercept: 1.0067 (0.006)
Ratio: 0.1065 (0.095)
Analysis finished at Thu Oct 17 14:41:44 2019
Total time elapsed: 1.0m:26.25s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9381,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 3,
    "n_p_sig": 434,
    "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": 67201,
    "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": 1271250,
    "ldsc_nsnp_merge_regression_ld": 1271250,
    "ldsc_observed_scale_h2_beta": 0.1115,
    "ldsc_observed_scale_h2_se": 0.0195,
    "ldsc_intercept_beta": 1.0067,
    "ldsc_intercept_se": 0.006,
    "ldsc_lambda_gc": 1.0561,
    "ldsc_mean_chisq": 1.063,
    "ldsc_ratio": 0.1063
}

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 7272548 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 7277268 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 8.663322e+00 5.763727e+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 7.863224e+07 5.645211e+07 828.0000000 3.215603e+07 6.905437e+07 1.145082e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 4.580000e-05 1.843870e-02 -0.1723310 -9.476200e-03 -1.740000e-05 9.542500e-03 1.947070e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.619830e-02 8.164300e-03 0.0085038 9.687700e-03 1.272750e-02 2.060830e-02 9.597950e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.926076e-01 2.910995e-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 4.926077e-01 2.910726e-01 0.0000000 2.382306e-01 4.902855e-01 7.447260e-01 9.999997e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.669054e-01 2.604575e-01 0.0137340 5.191400e-02 1.668220e-01 4.199890e-01 9.862650e-01 ▇▂▂▁▁
numeric AF_reference 67201 0.9907656 NA NA NA NA NA NA NA 2.655311e-01 2.523606e-01 0.0000000 5.830670e-02 1.797120e-01 4.135380e-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 -0.0119089 0.0156812 0.4500005 0.4475906 0.624922 0.7821490 NA
1 54676 rs2462492 C T -0.0051312 0.0155344 0.7400005 0.7411647 0.397912 NA NA
1 86028 rs114608975 T C 0.0023656 0.0245743 0.9199999 0.9233103 0.103681 0.0277556 NA
1 91536 rs6702460 G T 0.0058497 0.0152056 0.6999999 0.7004565 0.457884 0.4207270 NA
1 234313 rs8179466 C T 0.0181120 0.0296014 0.5400003 0.5406284 0.075119 NA NA
1 534192 rs6680723 C T -0.0271602 0.0174580 0.1199999 0.1197687 0.240099 NA NA
1 546697 rs12025928 A G -0.0054516 0.0218342 0.8000000 0.8028339 0.912738 NA NA
1 693731 rs12238997 A G 0.0165714 0.0146243 0.2599998 0.2571549 0.116766 0.1417730 NA
1 705882 rs72631875 G A -0.0141347 0.0211579 0.5000000 0.5040967 0.068727 0.0315495 NA
1 706368 rs55727773 A G -0.0149823 0.0108585 0.1700000 0.1676556 0.518329 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0039704 0.0132715 0.7600007 0.7648123 0.134145 0.2052720 NA
22 51219387 rs9616832 T C -0.0090333 0.0171972 0.5999997 0.5993896 0.072476 0.0654952 NA
22 51219704 rs147475742 G A -0.0267821 0.0231260 0.2500000 0.2468254 0.041277 0.0473243 NA
22 51221190 rs369304721 G A -0.0059604 0.0231239 0.8000000 0.7965920 0.048656 NA NA
22 51221731 rs115055839 T C -0.0086006 0.0172034 0.6200004 0.6171201 0.072038 0.0625000 NA
22 51222100 rs114553188 G T 0.0011141 0.0205765 0.9599999 0.9568190 0.051948 0.0880591 NA
22 51223637 rs375798137 G A 0.0004980 0.0206631 0.9800000 0.9807723 0.051649 0.0788738 NA
22 51229805 rs9616985 T C -0.0087954 0.0172858 0.6100002 0.6108772 0.071810 0.0730831 NA
22 51232488 rs376461333 A G 0.0061224 0.0422115 0.8800001 0.8846775 0.018712 NA NA
22 51237063 rs3896457 T C 0.0032209 0.0104263 0.7600007 0.7573833 0.296779 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.624922 ES:SE:LP:AF:ID  -0.0119089:0.0156812:0.346787:0.624922:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.397912 ES:SE:LP:AF:ID  -0.00513119:0.0155344:0.130768:0.397912:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103681 ES:SE:LP:AF:ID  0.00236564:0.0245743:0.0362122:0.103681:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457884 ES:SE:LP:AF:ID  0.00584966:0.0152056:0.154902:0.457884:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.075119 ES:SE:LP:AF:ID  0.018112:0.0296014:0.267606:0.075119:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240099 ES:SE:LP:AF:ID  -0.0271602:0.017458:0.920819:0.240099:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912738 ES:SE:LP:AF:ID  -0.00545159:0.0218342:0.09691:0.912738:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116766 ES:SE:LP:AF:ID  0.0165714:0.0146243:0.585027:0.116766:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.068727 ES:SE:LP:AF:ID  -0.0141347:0.0211579:0.30103:0.068727:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.518329 ES:SE:LP:AF:ID  -0.0149823:0.0108585:0.769551:0.518329:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032479 ES:SE:LP:AF:ID  0.00153708:0.0275085:0.0177288:0.032479:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.035835 ES:SE:LP:AF:ID  9.77783e-05:0.0251278:-0:0.035835:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.035947 ES:SE:LP:AF:ID  0.000426559:0.0250278:0.00436481:0.035947:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.035701 ES:SE:LP:AF:ID  0.0021876:0.0251937:0.0315171:0.035701:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016196 ES:SE:LP:AF:ID  -0.0237688:0.0387024:0.267606:0.016196:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036274 ES:SE:LP:AF:ID  0.00234262:0.0248974:0.0315171:0.036274:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036296 ES:SE:LP:AF:ID  0.00228672:0.0248333:0.0315171:0.036296:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.099758 ES:SE:LP:AF:ID  0.00402392:0.0179728:0.0861861:0.099758:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.960017 ES:SE:LP:AF:ID  -0.0100583:0.0239834:0.173925:0.960017:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031384 ES:SE:LP:AF:ID  0.026242:0.0431248:0.267606:0.031384:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053356 ES:SE:LP:AF:ID  0.0153863:0.034219:0.187087:0.053356:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03593  ES:SE:LP:AF:ID  0.00190151:0.02493:0.0268721:0.03593:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036111 ES:SE:LP:AF:ID  -0.00120079:0.0247753:0.0177288:0.036111:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843563 ES:SE:LP:AF:ID  -0.017308:0.0127338:0.769551:0.843563:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055985 ES:SE:LP:AF:ID  0.0227223:0.0205221:0.568636:0.055985:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122511 ES:SE:LP:AF:ID  0.0162649:0.0139077:0.619789:0.122511:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.0246   ES:SE:LP:AF:ID  0.0211171:0.0351313:0.259637:0.0246:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121739 ES:SE:LP:AF:ID  0.0165353:0.0139174:0.638272:0.121739:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132214 ES:SE:LP:AF:ID  0.0248665:0.0136958:1.16115:0.132214:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.035856 ES:SE:LP:AF:ID  0.000528508:0.0245677:0.00877392:0.035856:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839132 ES:SE:LP:AF:ID  -0.0156599:0.0123195:0.69897:0.839132:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83899  ES:SE:LP:AF:ID  -0.016185:0.0123116:0.721246:0.83899:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869302 ES:SE:LP:AF:ID  -0.0163066:0.0131725:0.657577:0.869302:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130191 ES:SE:LP:AF:ID  0.0164876:0.0132054:0.677781:0.130191:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.036466 ES:SE:LP:AF:ID  0.00141368:0.024093:0.0222764:0.036466:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.036706 ES:SE:LP:AF:ID  0.000415106:0.0239424:0.00436481:0.036706:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86876  ES:SE:LP:AF:ID  -0.0168138:0.0131488:0.69897:0.86876:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868864 ES:SE:LP:AF:ID  -0.0164211:0.0131528:0.677781:0.868864:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.036716 ES:SE:LP:AF:ID  0.0031111:0.0240455:0.0457575:0.036716:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868762 ES:SE:LP:AF:ID  -0.0168117:0.0131482:0.69897:0.868762:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838288 ES:SE:LP:AF:ID  -0.0165717:0.0122724:0.744727:0.838288:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.036762 ES:SE:LP:AF:ID  0.00284695:0.0240721:0.0409586:0.036762:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838795 ES:SE:LP:AF:ID  -0.0167035:0.0123019:0.769551:0.838795:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013821 ES:SE:LP:AF:ID  0.0415227:0.0429192:0.481486:0.013821:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.839943 ES:SE:LP:AF:ID  -0.016249:0.012465:0.721246:0.839943:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868914 ES:SE:LP:AF:ID  -0.0168323:0.013128:0.69897:0.868914:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868457 ES:SE:LP:AF:ID  -0.0180567:0.0130929:0.769551:0.868457:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.86747  ES:SE:LP:AF:ID  -0.0154951:0.0130722:0.619789:0.86747:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868542 ES:SE:LP:AF:ID  -0.0173654:0.0131047:0.721246:0.868542:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.868548 ES:SE:LP:AF:ID  -0.017373:0.0131055:0.744727:0.868548:rs4951862