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_93.vcf.gz --id UKB-b:6503 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_93.txt.gz --cohort_controls 39749 --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-6503/UKB-b-6503_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6503/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-6503/UKB-b-6503_data.vcf.gz ...
Read summary statistics for 7904694 SNPs.
Dropped 5961 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, 1281055 SNPs remain.
After merging with regression SNP LD, 1281055 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1247 (0.0128)
Lambda GC: 1.1114
Mean Chi^2: 1.1157
Intercept: 1.0179 (0.0065)
Ratio: 0.155 (0.0562)
Analysis finished at Thu Oct 17 14:41:53 2019
Total time elapsed: 1.0m:35.26s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9421,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 2,
    "n_p_sig": 35,
    "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": 73737,
    "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": 1281055,
    "ldsc_nsnp_merge_regression_ld": 1281055,
    "ldsc_observed_scale_h2_beta": 0.1247,
    "ldsc_observed_scale_h2_se": 0.0128,
    "ldsc_intercept_beta": 1.0179,
    "ldsc_intercept_se": 0.0065,
    "ldsc_lambda_gc": 1.1114,
    "ldsc_mean_chisq": 1.1157,
    "ldsc_ratio": 0.1547
}

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 7898760 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 7904694 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 8.660725e+00 5.763708e+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 7.870419e+07 5.642640e+07 828.0000000 3.224837e+07 6.916675e+07 1.145573e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.418000e-04 1.718040e-02 -0.1690730 -8.288200e-03 -5.630000e-05 8.084500e-03 1.698050e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.447020e-02 8.535500e-03 0.0066964 7.747800e-03 1.070550e-02 1.895970e-02 8.099150e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.869498e-01 2.922594e-01 0.0000000 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.869505e-01 2.922342e-01 0.0000000 2.304958e-01 4.820604e-01 7.399424e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.481216e-01 2.608182e-01 0.0088060 3.715500e-02 1.401870e-01 3.929430e-01 9.911940e-01 ▇▂▂▁▁
numeric AF_reference 73737 0.9906717 NA NA NA NA NA NA NA 2.472700e-01 2.526217e-01 0.0000000 3.953670e-02 1.553510e-01 3.877800e-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 -0.0153359 0.0122278 0.2099999 0.2097755 0.621393 0.7821490 NA
1 54676 rs2462492 C T -0.0016107 0.0121526 0.8900000 0.8945562 0.401510 NA NA
1 86028 rs114608975 T C -0.0038236 0.0191648 0.8400000 0.8418613 0.104451 0.0277556 NA
1 91536 rs6702460 G T 0.0011967 0.0119279 0.9199999 0.9200828 0.457337 0.4207270 NA
1 234313 rs8179466 C T 0.0509315 0.0235565 0.0309999 0.0306105 0.073935 NA NA
1 534192 rs6680723 C T -0.0178836 0.0136080 0.1900002 0.1887797 0.241068 NA NA
1 546697 rs12025928 A G -0.0131196 0.0172526 0.4500005 0.4469905 0.915206 NA NA
1 693731 rs12238997 A G -0.0150415 0.0116691 0.2000000 0.1973972 0.113460 0.1417730 NA
1 705882 rs72631875 G A -0.0111008 0.0169749 0.5099998 0.5131416 0.066131 0.0315495 NA
1 706368 rs55727773 A G 0.0039112 0.0085164 0.6499995 0.6460507 0.514760 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0165220 0.0102911 0.1100001 0.1083915 0.138239 0.2052720 NA
22 51219387 rs9616832 T C 0.0078289 0.0131326 0.5500004 0.5510806 0.076701 0.0654952 NA
22 51219704 rs147475742 G A 0.0115773 0.0176444 0.5099998 0.5117303 0.043303 0.0473243 NA
22 51221190 rs369304721 G A 0.0176639 0.0174497 0.3100002 0.3114064 0.052223 NA NA
22 51221731 rs115055839 T C 0.0079949 0.0131419 0.5400003 0.5429551 0.076180 0.0625000 NA
22 51222100 rs114553188 G T 0.0180233 0.0160886 0.2599998 0.2626060 0.052689 0.0880591 NA
22 51223637 rs375798137 G A 0.0185926 0.0161672 0.2500000 0.2501357 0.052307 0.0788738 NA
22 51229805 rs9616985 T C 0.0077715 0.0132043 0.5600000 0.5561576 0.075823 0.0730831 NA
22 51232488 rs376461333 A G 0.0272640 0.0322496 0.4000000 0.3978843 0.019190 NA NA
22 51237063 rs3896457 T C -0.0202575 0.0082338 0.0140001 0.0138825 0.299456 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.621393 ES:SE:LP:AF:ID  -0.0153359:0.0122278:0.677781:0.621393:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.40151  ES:SE:LP:AF:ID  -0.00161072:0.0121526:0.05061:0.40151:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104451 ES:SE:LP:AF:ID  -0.00382363:0.0191648:0.0757207:0.104451:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457337 ES:SE:LP:AF:ID  0.00119672:0.0119279:0.0362122:0.457337:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.073935 ES:SE:LP:AF:ID  0.0509315:0.0235565:1.50864:0.073935:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241068 ES:SE:LP:AF:ID  -0.0178836:0.013608:0.721246:0.241068:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.915206 ES:SE:LP:AF:ID  -0.0131196:0.0172526:0.346787:0.915206:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11346  ES:SE:LP:AF:ID  -0.0150415:0.0116691:0.69897:0.11346:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.066131 ES:SE:LP:AF:ID  -0.0111008:0.0169749:0.29243:0.066131:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51476  ES:SE:LP:AF:ID  0.00391119:0.00851637:0.187087:0.51476:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032191 ES:SE:LP:AF:ID  -0.0330037:0.0217275:0.886057:0.032191:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.035725 ES:SE:LP:AF:ID  -0.0304217:0.019757:0.920819:0.035725:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.035777 ES:SE:LP:AF:ID  -0.0310546:0.0197076:0.920819:0.035777:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.035528 ES:SE:LP:AF:ID  -0.032309:0.0198379:1:0.035528:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016184 ES:SE:LP:AF:ID  -0.0204388:0.0302634:0.30103:0.016184:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036004 ES:SE:LP:AF:ID  -0.0341908:0.0196283:1.08619:0.036004:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036147 ES:SE:LP:AF:ID  -0.0309366:0.0195418:0.958607:0.036147:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.098971 ES:SE:LP:AF:ID  0.000948383:0.0141933:0.0222764:0.098971:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.960699 ES:SE:LP:AF:ID  0.0309303:0.0189727:1:0.960699:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.0302   ES:SE:LP:AF:ID  0.010096:0.0351521:0.113509:0.0302:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.054738 ES:SE:LP:AF:ID  0.0230418:0.0260263:0.420216:0.054738:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.035551 ES:SE:LP:AF:ID  -0.0309881:0.0197203:0.920819:0.035551:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03591  ES:SE:LP:AF:ID  -0.0256296:0.0195098:0.721246:0.03591:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.847513 ES:SE:LP:AF:ID  0.0193648:0.0101066:1.25964:0.847513:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055223 ES:SE:LP:AF:ID  -0.0047686:0.0162216:0.113509:0.055223:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.119463 ES:SE:LP:AF:ID  -0.0103933:0.0110635:0.455932:0.119463:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.026622 ES:SE:LP:AF:ID  -0.0025458:0.0264549:0.0362122:0.026622:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.118702 ES:SE:LP:AF:ID  -0.011768:0.0110663:0.537602:0.118702:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.129102 ES:SE:LP:AF:ID  -0.0209375:0.010872:1.26761:0.129102:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011191 ES:SE:LP:AF:ID  0.00493359:0.0389926:0.0457575:0.011191:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.035799 ES:SE:LP:AF:ID  -0.0282209:0.019323:0.853872:0.035799:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.843164 ES:SE:LP:AF:ID  0.0225118:0.00978648:1.67778:0.843164:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.84294  ES:SE:LP:AF:ID  0.0228778:0.00977689:1.72125:0.84294:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.873098 ES:SE:LP:AF:ID  0.0159677:0.0105085:0.886057:0.873098:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.126507 ES:SE:LP:AF:ID  -0.0168542:0.010526:0.958607:0.126507:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.036427 ES:SE:LP:AF:ID  -0.0264309:0.0189691:0.79588:0.036427:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.036658 ES:SE:LP:AF:ID  -0.0268466:0.018852:0.823909:0.036658:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.872659 ES:SE:LP:AF:ID  0.0166968:0.010491:0.958607:0.872659:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.872744 ES:SE:LP:AF:ID  0.0168886:0.0104956:0.958607:0.872744:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.036599 ES:SE:LP:AF:ID  -0.0272239:0.0189313:0.823909:0.036599:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.87267  ES:SE:LP:AF:ID  0.0165625:0.0104921:0.958607:0.87267:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.84246  ES:SE:LP:AF:ID  0.0226952:0.00975338:1.69897:0.84246:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.036598 ES:SE:LP:AF:ID  -0.0276815:0.018958:0.853872:0.036598:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.843046 ES:SE:LP:AF:ID  0.0228534:0.00978024:1.72125:0.843046:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.015369 ES:SE:LP:AF:ID  -0.0284216:0.0317229:0.431798:0.015369:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.843502 ES:SE:LP:AF:ID  0.0216541:0.00989831:1.5376:0.843502:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.872656 ES:SE:LP:AF:ID  0.0159136:0.0104739:0.886057:0.872656:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.872201 ES:SE:LP:AF:ID  0.0161932:0.0104472:0.920819:0.872201:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.87156  ES:SE:LP:AF:ID  0.017114:0.0104372:1:0.87156:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.872379 ES:SE:LP:AF:ID  0.0162035:0.0104569:0.920819:0.872379:rs4951929