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_6141_3.vcf.gz --id UKB-b:374 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_6141_3.txt.gz --cohort_cases 2511 --cohort_controls 457089 --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-374/UKB-b-374_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-374/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:46 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-374/UKB-b-374_data.vcf.gz ...
Read summary statistics for 3670858 SNPs.
Dropped 617 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, 889458 SNPs remain.
After merging with regression SNP LD, 889458 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0049 (0.0015)
Lambda GC: 1.1513
Mean Chi^2: 1.1603
Intercept: 1.1105 (0.0092)
Ratio: 0.6893 (0.0577)
Analysis finished at Thu Oct 17 14:44:35 2019
Total time elapsed: 49.04s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.8606,
    "inflation_factor": 1.1474,
    "mean_EFFECT": -2.0548e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 3,
    "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": 29609,
    "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": 889458,
    "ldsc_nsnp_merge_regression_ld": 889458,
    "ldsc_observed_scale_h2_beta": 0.0049,
    "ldsc_observed_scale_h2_se": 0.0015,
    "ldsc_intercept_beta": 1.1105,
    "ldsc_intercept_se": 0.0092,
    "ldsc_lambda_gc": 1.1513,
    "ldsc_mean_chisq": 1.1603,
    "ldsc_ratio": 0.6893
}

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 TRUE
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 1.000000 3 58 0 3670244 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 3670858 0.000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.000000 NA NA NA NA NA NA NA 8.661655e+00 5.772025e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.000000 NA NA NA NA NA NA NA 7.855058e+07 5.677009e+07 828.0000000 3.157490e+07 6.888193e+07 1.147239e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA -2.100000e-06 1.897000e-04 -0.0010492 -1.275000e-04 -2.400000e-06 1.236000e-04 1.109300e-03 ▁▂▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 1.752000e-04 2.010000e-05 0.0001482 1.577000e-04 1.687000e-04 1.892000e-04 5.419000e-04 ▇▁▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.770560e-01 2.949818e-01 0.0000000 2.200002e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.770554e-01 2.949563e-01 0.0000000 2.151217e-01 4.696262e-01 7.324096e-01 9.999998e-01 ▇▇▆▆▆
numeric AF 0 1.000000 NA NA NA NA NA NA NA 4.080389e-01 2.017676e-01 0.1393870 2.313420e-01 3.661030e-01 5.603080e-01 8.606130e-01 ▇▅▃▃▂
numeric AF_reference 29609 0.991934 NA NA NA NA NA NA NA 3.952790e-01 2.093588e-01 0.0000000 2.236420e-01 3.606230e-01 5.477240e-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 0.0000829 0.0002726 0.7600007 0.7609414 0.623772 0.782149 NA
1 54676 rs2462492 C T -0.0000417 0.0002701 0.8800001 0.8774475 0.400415 NA NA
1 91536 rs6702460 G T 0.0000076 0.0002660 0.9800000 0.9773308 0.456897 0.420727 NA
1 534192 rs6680723 C T 0.0005435 0.0003038 0.0739997 0.0736603 0.240946 NA NA
1 706368 rs55727773 A G 0.0001934 0.0001886 0.3100002 0.3050929 0.515651 0.275160 NA
1 729679 rs4951859 C G 0.0000014 0.0002207 1.0000000 0.9950603 0.843240 0.639976 NA
1 752566 rs3094315 G A 0.0000193 0.0002137 0.9299999 0.9278703 0.838960 0.718251 NA
1 752721 rs3131972 A G -0.0000156 0.0002135 0.9400001 0.9416681 0.838590 0.653355 NA
1 754503 rs3115859 G A -0.0000064 0.0002129 0.9800000 0.9758396 0.838041 0.663938 NA
1 754964 rs3131966 C T -0.0000118 0.0002135 0.9599999 0.9559416 0.838673 0.663339 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51182485 rs6009961 A G 0.0002365 0.0001781 0.1800002 0.1843358 0.715548 0.6383790 NA
22 51186143 rs2879914 T C 0.0000492 0.0001652 0.7700005 0.7657391 0.381853 0.2733630 NA
22 51186228 rs3865766 C T 0.0000576 0.0001610 0.7199992 0.7205501 0.451096 0.4532750 NA
22 51192586 rs5771006 G A -0.0000911 0.0002169 0.6700003 0.6744157 0.167637 0.0848642 NA
22 51193227 rs34608236 T G 0.0000309 0.0002217 0.8900000 0.8892109 0.168491 0.0692891 NA
22 51197266 rs61290853 A G 0.0001989 0.0001662 0.2300001 0.2314479 0.386375 0.4229230 NA
22 51198027 rs34939255 A G 0.0002445 0.0001882 0.1900002 0.1936981 0.254580 0.0984425 NA
22 51211106 rs9628250 T C 0.0002384 0.0001865 0.2000000 0.2012183 0.271571 0.1671330 NA
22 51212875 rs2238837 A C -0.0000456 0.0001772 0.8000000 0.7970718 0.331473 0.3724040 NA
22 51237063 rs3896457 T C 0.0000593 0.0001814 0.7400005 0.7438576 0.298011 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623772 ES:SE:LP:AF:ID  8.29496e-05:0.000272641:0.119186:0.623772:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400415 ES:SE:LP:AF:ID  -4.16516e-05:0.000270104:0.0555173:0.400415:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.456897 ES:SE:LP:AF:ID  7.55746e-06:0.000265963:0.00877392:0.456897:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.240946 ES:SE:LP:AF:ID  0.000543484:0.00030384:1.13077:0.240946:rs6680723
1   706368  rs12029736  A   G   .   PASS    AF=0.515651 ES:SE:LP:AF:ID  0.00019342:0.000188597:0.508638:0.515651:rs12029736
1   729679  rs4951859   C   G   .   PASS    AF=0.84324  ES:SE:LP:AF:ID  1.36614e-06:0.000220666:-0:0.84324:rs4951859
1   752566  rs3094315   G   A   .   PASS    AF=0.83896  ES:SE:LP:AF:ID  1.93444e-05:0.000213692:0.0315171:0.83896:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83859  ES:SE:LP:AF:ID  -1.56199e-05:0.000213464:0.0268721:0.83859:rs3131972
1   754503  rs3115859   G   A   .   PASS    AF=0.838041 ES:SE:LP:AF:ID  -6.44679e-06:0.000212869:0.00877392:0.838041:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.838673 ES:SE:LP:AF:ID  -1.17935e-05:0.000213468:0.0177288:0.838673:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.83978  ES:SE:LP:AF:ID  2.22602e-05:0.00021635:0.0362122:0.83978:rs3131965
1   760912  rs1048488   C   T   .   PASS    AF=0.83832  ES:SE:LP:AF:ID  2.39972e-05:0.000212461:0.0409586:0.83832:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.83844  ES:SE:LP:AF:ID  2.13391e-05:0.000212611:0.0362122:0.83844:rs3115850
1   763394  rs3115847   G   A   .   PASS    AF=0.706756 ES:SE:LP:AF:ID  0.000295618:0.000221136:0.744727:0.706756:rs3115847
1   768253  rs2977608   A   C   .   PASS    AF=0.761378 ES:SE:LP:AF:ID  9.60054e-06:0.000180483:0.0177288:0.761378:rs2977608
1   776546  rs12124819  A   G   .   PASS    AF=0.26542  ES:SE:LP:AF:ID  0.000150727:0.000201564:0.346787:0.26542:rs12124819
1   795988  rs59380221  C   T   .   PASS    AF=0.143033 ES:SE:LP:AF:ID  -4.4496e-05:0.000236418:0.0705811:0.143033:rs59380221
1   798400  rs10900604  A   G   .   PASS    AF=0.206547 ES:SE:LP:AF:ID  7.80468e-05:0.00019253:0.161151:0.206547:rs10900604
1   798959  rs11240777  G   A   .   PASS    AF=0.206375 ES:SE:LP:AF:ID  8.00223e-05:0.000192613:0.167491:0.206375:rs11240777
1   808631  rs11240779  G   A   .   PASS    AF=0.772679 ES:SE:LP:AF:ID  -5.51418e-05:0.000183227:0.119186:0.772679:rs11240779
1   808928  rs11240780  C   T   .   PASS    AF=0.772905 ES:SE:LP:AF:ID  -6.05711e-05:0.000183536:0.130768:0.772905:rs11240780
1   814495  rs74461805  C   A   .   PASS    AF=0.340433 ES:SE:LP:AF:ID  2.43348e-05:0.000258596:0.0315171:0.340433:rs74461805
1   830181  rs28444699  A   G   .   PASS    AF=0.697236 ES:SE:LP:AF:ID  4.48146e-05:0.000172999:0.09691:0.697236:rs28444699
1   831489  rs4970385   C   T   .   PASS    AF=0.705385 ES:SE:LP:AF:ID  -2.94406e-06:0.000169881:0.00436481:0.705385:rs4970385
1   831909  rs9697642   C   T   .   PASS    AF=0.705429 ES:SE:LP:AF:ID  -9.18622e-07:0.000169875:-0:0.705429:rs9697642
1   832066  rs9697380   G   C   .   PASS    AF=0.705614 ES:SE:LP:AF:ID  2.03605e-06:0.000169883:0.00436481:0.705614:rs9697380
1   832318  rs4500250   C   A   .   PASS    AF=0.705643 ES:SE:LP:AF:ID  2.95158e-06:0.000169901:0.00436481:0.705643:rs4500250
1   832398  rs4553118   T   C   .   PASS    AF=0.730111 ES:SE:LP:AF:ID  4.40912e-05:0.000174532:0.09691:0.730111:rs4553118
1   832918  rs28765502  T   C   .   PASS    AF=0.29439  ES:SE:LP:AF:ID  -1.14845e-06:0.000169893:0.00436481:0.29439:rs28765502
1   833223  rs13303211  C   T   .   PASS    AF=0.236743 ES:SE:LP:AF:ID  -9.03552e-05:0.000180871:0.207608:0.236743:rs13303211
1   833302  rs28752186  C   T   .   PASS    AF=0.236731 ES:SE:LP:AF:ID  -8.98722e-05:0.000180872:0.207608:0.236731:rs28752186
1   833641  rs28594623  T   C   .   PASS    AF=0.23979  ES:SE:LP:AF:ID  -9.06078e-05:0.000180291:0.207608:0.23979:rs28594623
1   833824  rs28484835  T   C   .   PASS    AF=0.236733 ES:SE:LP:AF:ID  -9.00172e-05:0.000180871:0.207608:0.236733:rs28484835
1   833927  rs28593608  T   C   .   PASS    AF=0.212465 ES:SE:LP:AF:ID  -0.000148956:0.000187986:0.366532:0.212465:rs28593608
1   834198  rs28385272  T   C   .   PASS    AF=0.21236  ES:SE:LP:AF:ID  -0.000150396:0.000188018:0.376751:0.21236:rs28385272
1   834832  rs4411087   G   C   .   PASS    AF=0.237217 ES:SE:LP:AF:ID  -7.23627e-05:0.000180732:0.161151:0.237217:rs4411087
1   834928  rs4422949   A   G   .   PASS    AF=0.213    ES:SE:LP:AF:ID  -0.000138484:0.000187753:0.337242:0.213:rs4422949
1   834999  rs28570054  G   A   .   PASS    AF=0.212963 ES:SE:LP:AF:ID  -0.000141507:0.00018779:0.346787:0.212963:rs28570054
1   835499  rs4422948   A   G   .   PASS    AF=0.241189 ES:SE:LP:AF:ID  -0.000119753:0.000179472:0.30103:0.241189:rs4422948
1   836529  rs28731045  C   G   .   PASS    AF=0.213584 ES:SE:LP:AF:ID  -0.000126681:0.000187512:0.30103:0.213584:rs28731045
1   836896  rs28705752  T   C   .   PASS    AF=0.269529 ES:SE:LP:AF:ID  -0.000125668:0.00017318:0.327902:0.269529:rs28705752
1   836924  rs72890788  G   A   .   PASS    AF=0.213555 ES:SE:LP:AF:ID  -0.000125839:0.000187535:0.30103:0.213555:rs72890788
1   838387  rs4970384   T   C   .   PASS    AF=0.21463  ES:SE:LP:AF:ID  -0.000121019:0.000187174:0.283997:0.21463:rs4970384
1   838555  rs4970383   C   A   .   PASS    AF=0.246225 ES:SE:LP:AF:ID  -0.000172379:0.000178242:0.481486:0.246225:rs4970383
1   839103  rs28562941  A   G   .   PASS    AF=0.270038 ES:SE:LP:AF:ID  -0.000129917:0.000173302:0.346787:0.270038:rs28562941
1   840753  rs4970382   T   C   .   PASS    AF=0.400141 ES:SE:LP:AF:ID  -9.68726e-05:0.000156693:0.267606:0.400141:rs4970382
1   841085  rs1574243   C   G   .   PASS    AF=0.237122 ES:SE:LP:AF:ID  -0.000224282:0.000182016:0.657577:0.237122:rs1574243
1   842013  rs7419119   T   G   .   PASS    AF=0.215424 ES:SE:LP:AF:ID  -0.000159741:0.000187304:0.408935:0.215424:rs7419119
1   842362  rs28540380  C   T   .   PASS    AF=0.235374 ES:SE:LP:AF:ID  -0.000286608:0.00018472:0.920819:0.235374:rs28540380
1   843405  rs11516185  A   G   .   PASS    AF=0.362613 ES:SE:LP:AF:ID  -4.03872e-05:0.00019449:0.0757207:0.362613:rs11516185