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_41202_Z538.vcf.gz --id UKB-b:9696 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_41202_Z538.txt.gz --cohort_cases 1155 --cohort_controls 461855 --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-9696/UKB-b-9696_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9696/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:51 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9696/UKB-b-9696_data.vcf.gz ...
Read summary statistics for 1781739 SNPs.
Dropped 169 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, 458882 SNPs remain.
After merging with regression SNP LD, 458882 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0019 (0.0016)
Lambda GC: 1.0782
Mean Chi^2: 1.0865
Intercept: 1.0654 (0.0124)
Ratio: 0.7558 (0.1433)
Analysis finished at Thu Oct 17 14:43:18 2019
Total time elapsed: 27.11s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.6544,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -8.03e-07,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 14090,
    "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": 458882,
    "ldsc_nsnp_merge_regression_ld": 458882,
    "ldsc_observed_scale_h2_beta": 0.0019,
    "ldsc_observed_scale_h2_se": 0.0016,
    "ldsc_intercept_beta": 1.0654,
    "ldsc_intercept_se": 0.0124,
    "ldsc_lambda_gc": 1.0782,
    "ldsc_mean_chisq": 1.0865,
    "ldsc_ratio": 0.7561
}
 

Flags

name value
af_correlation TRUE
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 4 58 0 1781572 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 1781739 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 8.655580e+00 5.766071e+00 1.00000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.000000 NA NA NA NA NA NA NA 7.869658e+07 5.664718e+07 1.23330e+04 3.178970e+07 6.928345e+07 1.148325e+08 2.491722e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA -8.000000e-07 1.117000e-04 -6.03900e-04 -7.540000e-05 -9.000000e-07 7.410000e-05 6.485000e-04 ▁▂▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 1.071000e-04 3.400000e-06 1.00000e-04 1.045000e-04 1.062000e-04 1.091000e-04 2.030000e-04 ▇▁▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.871137e-01 2.923389e-01 1.10000e-05 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▆
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.871178e-01 2.923101e-01 1.14000e-05 2.296634e-01 4.848795e-01 7.402635e-01 9.999996e-01 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA NA NA 4.754184e-01 1.129396e-01 3.03031e-01 3.760965e-01 4.629150e-01 5.684965e-01 6.969680e-01 ▇▇▆▅▅
numeric AF_reference 14090 0.992092 NA NA NA NA NA NA NA 4.544419e-01 1.536519e-01 1.99700e-04 3.376600e-01 4.466850e-01 5.650960e-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 0.0000833 0.0001839 0.6499995 0.6505504 0.623765 0.782149 NA
1 54676 rs2462492 C T -0.0001905 0.0001822 0.2999998 0.2959100 0.400401 NA NA
1 91536 rs6702460 G T -0.0000388 0.0001794 0.8300000 0.8286370 0.456846 0.420727 NA
1 706368 rs55727773 A G -0.0000891 0.0001272 0.4799997 0.4836873 0.515645 0.275160 NA
1 814495 rs74461805 C A 0.0000943 0.0001745 0.5900000 0.5886849 0.340396 NA NA
1 840753 rs4970382 T C -0.0000375 0.0001057 0.7199992 0.7226111 0.400124 0.468850 NA
1 843405 rs11516185 A G -0.0001528 0.0001312 0.2399999 0.2443351 0.362606 0.375399 NA
1 850218 rs6664536 T A -0.0001791 0.0001054 0.0890000 0.0893208 0.590331 0.345248 NA
1 850371 rs6679046 G T -0.0001623 0.0001060 0.1299999 0.1257764 0.603723 0.508786 NA
1 850780 rs6657440 C T -0.0001724 0.0001060 0.1000000 0.1038506 0.603942 0.560304 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51163039 rs715584 G T -0.0001058 0.0001053 0.3200000 0.3151740 0.426975 0.473642 NA
22 51164109 rs5770995 G C -0.0000700 0.0001047 0.5000000 0.5036875 0.452705 0.510982 NA
22 51164115 rs5770996 C T -0.0000641 0.0001046 0.5400003 0.5403405 0.456917 0.514776 NA
22 51164287 rs6009957 T C -0.0000892 0.0001125 0.4299995 0.4281415 0.306541 0.415535 NA
22 51174048 rs9628245 G C -0.0001226 0.0001182 0.2999998 0.2996310 0.380135 0.433107 NA
22 51181919 rs9616825 G C 0.0001088 0.0001192 0.3599996 0.3612669 0.695470 0.619409 NA
22 51186143 rs2879914 T C -0.0000777 0.0001114 0.4899999 0.4855488 0.381825 0.273363 NA
22 51186228 rs3865766 C T -0.0000528 0.0001086 0.6300007 0.6264733 0.451061 0.453275 NA
22 51197266 rs61290853 A G -0.0000447 0.0001121 0.6899999 0.6900851 0.386333 0.422923 NA
22 51212875 rs2238837 A C -0.0000205 0.0001196 0.8600001 0.8639466 0.331457 0.372404 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623765 ES:SE:LP:AF:ID  8.33262e-05:0.000183944:0.187087:0.623765:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  -0.000190477:0.000182232:0.522879:0.400401:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.456846 ES:SE:LP:AF:ID  -3.88374e-05:0.000179429:0.0809219:0.456846:rs6702460
1   706368  rs12029736  A   G   .   PASS    AF=0.515645 ES:SE:LP:AF:ID  -8.91113e-05:0.000127232:0.318759:0.515645:rs12029736
1   814495  rs74461805  C   A   .   PASS    AF=0.340396 ES:SE:LP:AF:ID  9.43369e-05:0.000174458:0.229148:0.340396:rs74461805
1   840753  rs4970382   T   C   .   PASS    AF=0.400124 ES:SE:LP:AF:ID  -3.75265e-05:0.000105717:0.142668:0.400124:rs4970382
1   843405  rs11516185  A   G   .   PASS    AF=0.362606 ES:SE:LP:AF:ID  -0.000152784:0.000131233:0.619789:0.362606:rs11516185
1   850218  rs6664536   T   A   .   PASS    AF=0.590331 ES:SE:LP:AF:ID  -0.000179089:0.000105409:1.05061:0.590331:rs6664536
1   850371  rs6679046   G   T   .   PASS    AF=0.603723 ES:SE:LP:AF:ID  -0.000162283:0.000106:0.886057:0.603723:rs6679046
1   850780  rs6657440   C   T   .   PASS    AF=0.603942 ES:SE:LP:AF:ID  -0.000172381:0.000105985:1:0.603942:rs6657440
1   852037  rs4970463   G   A   .   PASS    AF=0.589686 ES:SE:LP:AF:ID  -0.000184719:0.00010558:1.09691:0.589686:rs4970463
1   852063  rs28436996  G   A   .   PASS    AF=0.589665 ES:SE:LP:AF:ID  -0.000185354:0.000105533:1.10237:0.589665:rs28436996
1   852875  rs13303369  C   T   .   PASS    AF=0.607671 ES:SE:LP:AF:ID  -0.000159197:0.000106222:0.886057:0.607671:rs13303369
1   853954  rs1806509   C   A   .   PASS    AF=0.607829 ES:SE:LP:AF:ID  -0.000158669:0.000106237:0.853872:0.607829:rs1806509
1   854777  rs13303019  A   G   .   PASS    AF=0.610316 ES:SE:LP:AF:ID  -0.000162129:0.000106341:0.886057:0.610316:rs13303019
1   854978  rs13303057  A   C   .   PASS    AF=0.603283 ES:SE:LP:AF:ID  -0.000157733:0.000106026:0.853872:0.603283:rs13303057
1   855075  rs6673914   C   G   .   PASS    AF=0.610337 ES:SE:LP:AF:ID  -0.000162485:0.000106343:0.886057:0.610337:rs6673914
1   856099  rs28534711  T   G   .   PASS    AF=0.389936 ES:SE:LP:AF:ID  0.000161685:0.000106363:0.886057:0.389936:rs28534711
1   856108  rs28742275  A   G   .   PASS    AF=0.38992  ES:SE:LP:AF:ID  0.000161788:0.000106369:0.886057:0.38992:rs28742275
1   856476  rs4040605   A   G   .   PASS    AF=0.350356 ES:SE:LP:AF:ID  0.000130363:0.000109271:0.638272:0.350356:rs4040605
1   866893  rs2880024   T   C   .   PASS    AF=0.610552 ES:SE:LP:AF:ID  -5.4763e-05:0.00010694:0.21467:0.610552:rs2880024
1   875770  rs4970379   A   G   .   PASS    AF=0.600085 ES:SE:LP:AF:ID  -2.91582e-05:0.000107829:0.102373:0.600085:rs4970379
1   881627  rs2272757   G   A   .   PASS    AF=0.652393 ES:SE:LP:AF:ID  -3.06022e-05:0.000108926:0.107905:0.652393:rs2272757
1   891059  rs13303065  C   T   .   PASS    AF=0.652432 ES:SE:LP:AF:ID  -2.70943e-05:0.000108909:0.09691:0.652432:rs13303065
1   891945  rs13303106  A   G   .   PASS    AF=0.652494 ES:SE:LP:AF:ID  -2.86376e-05:0.000109036:0.102373:0.652494:rs13303106
1   903245  rs28690976  A   G   .   PASS    AF=0.566938 ES:SE:LP:AF:ID  4.76339e-05:0.000108296:0.180456:0.566938:rs28690976
1   909073  rs3892467   C   T   .   PASS    AF=0.386681 ES:SE:LP:AF:ID  6.65724e-05:0.000108002:0.267606:0.386681:rs3892467
1   909238  rs3829740   G   C   .   PASS    AF=0.571408 ES:SE:LP:AF:ID  -4.16485e-05:0.000104597:0.161151:0.571408:rs3829740
1   910394  rs28477686  C   T   .   PASS    AF=0.324458 ES:SE:LP:AF:ID  -0.000172146:0.000113371:0.886057:0.324458:rs28477686
1   912049  rs7367995   T   C   .   PASS    AF=0.585249 ES:SE:LP:AF:ID  0.000116466:0.000105657:0.568636:0.585249:rs7367995
1   913889  rs2340596   G   A   .   PASS    AF=0.59921  ES:SE:LP:AF:ID  0.000131821:0.000105829:0.677781:0.59921:rs2340596
1   914333  rs13302979  C   G   .   PASS    AF=0.602516 ES:SE:LP:AF:ID  0.000107211:0.000106149:0.508638:0.602516:rs13302979
1   914852  rs13303368  G   C   .   PASS    AF=0.600074 ES:SE:LP:AF:ID  0.000129665:0.000105945:0.657577:0.600074:rs13303368
1   914940  rs13303033  T   C   .   PASS    AF=0.584289 ES:SE:LP:AF:ID  9.87871e-05:0.000105352:0.455932:0.584289:rs13303033
1   916834  rs6694632   G   A   .   PASS    AF=0.589102 ES:SE:LP:AF:ID  9.61283e-05:0.000105513:0.443698:0.589102:rs6694632
1   918384  rs13303118  G   T   .   PASS    AF=0.584202 ES:SE:LP:AF:ID  9.31549e-05:0.000105302:0.420216:0.584202:rs13303118
1   918573  rs2341354   A   G   .   PASS    AF=0.589327 ES:SE:LP:AF:ID  9.59164e-05:0.000105432:0.443698:0.589327:rs2341354
1   919501  rs4970414   G   T   .   PASS    AF=0.583926 ES:SE:LP:AF:ID  0.000108794:0.000109034:0.49485:0.583926:rs4970414
1   921716  rs13303278  C   A   .   PASS    AF=0.567888 ES:SE:LP:AF:ID  7.99011e-06:0.000105214:0.0268721:0.567888:rs13303278
1   924528  rs34712273  C   A   .   PASS    AF=0.578491 ES:SE:LP:AF:ID  4.82537e-05:0.000105516:0.187087:0.578491:rs34712273
1   930533  rs3128110   C   G   .   PASS    AF=0.386082 ES:SE:LP:AF:ID  -6.45022e-05:0.000106863:0.259637:0.386082:rs3128110
1   930567  rs3121574   A   G   .   PASS    AF=0.386124 ES:SE:LP:AF:ID  -6.50363e-05:0.000106866:0.267606:0.386124:rs3121574
1   930751  rs3128111   C   G   .   PASS    AF=0.385079 ES:SE:LP:AF:ID  -5.6162e-05:0.000106931:0.221849:0.385079:rs3128111
1   931166  rs2710880   A   G   .   PASS    AF=0.386765 ES:SE:LP:AF:ID  -6.81683e-05:0.000106883:0.283997:0.386765:rs2710880
1   931362  rs2799060   G   A   .   PASS    AF=0.385571 ES:SE:LP:AF:ID  -6.05707e-05:0.000106935:0.244125:0.385571:rs2799060
1   933790  rs9442392   G   A   .   PASS    AF=0.578604 ES:SE:LP:AF:ID  3.62963e-05:0.00010548:0.136677:0.578604:rs9442392
1   936111  rs1936360   C   T   .   PASS    AF=0.573545 ES:SE:LP:AF:ID  2.55689e-05:0.000105455:0.091515:0.573545:rs1936360
1   940005  rs2799056   A   G   .   PASS    AF=0.399264 ES:SE:LP:AF:ID  -5.4412e-06:0.000106493:0.0177288:0.399264:rs2799056
1   940096  rs4503294   C   T   .   PASS    AF=0.565308 ES:SE:LP:AF:ID  -3.04355e-05:0.000105153:0.113509:0.565308:rs4503294
1   941284  rs3128116   C   T   .   PASS    AF=0.397398 ES:SE:LP:AF:ID  2.70554e-06:0.000106516:0.00877392:0.397398:rs3128116