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_20003_1195.vcf.gz --id UKB-b:12466 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20003_1195.txt.gz --cohort_cases 1222 --cohort_controls 461711 --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-12466/UKB-b-12466_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12466/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:56 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12466/UKB-b-12466_data.vcf.gz ...
Read summary statistics for 1945815 SNPs.
Dropped 187 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, 499249 SNPs remain.
After merging with regression SNP LD, 499249 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0017 (0.0013)
Lambda GC: 1.0138
Mean Chi^2: 1.0077
Intercept: 0.9889 (0.0108)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:42:26 2019
Total time elapsed: 30.71s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.6864,
    "inflation_factor": 1,
    "mean_EFFECT": -6.0422e-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": 15423,
    "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": 499249,
    "ldsc_nsnp_merge_regression_ld": 499249,
    "ldsc_observed_scale_h2_beta": 0.0017,
    "ldsc_observed_scale_h2_se": 0.0013,
    "ldsc_intercept_beta": 0.9889,
    "ldsc_intercept_se": 0.0108,
    "ldsc_lambda_gc": 1.0138,
    "ldsc_mean_chisq": 1.0077,
    "ldsc_ratio": -1.4416
}
 

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 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 4 58 0 1945630 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 1945815 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 8.649196e+00 5.762248e+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 7.871973e+07 5.663206e+07 5687.0000000 3.184246e+07 6.927702e+07 1.148791e+08 2.491722e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -6.000000e-07 1.115000e-04 -0.0006262 -7.580000e-05 -3.000000e-07 7.450000e-05 6.350000e-04 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.109000e-04 4.000000e-06 0.0001029 1.077000e-04 1.098000e-04 1.134000e-04 2.111000e-04 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.985545e-01 2.890610e-01 0.0000001 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 4.985496e-01 2.890333e-01 0.0000001 2.486029e-01 4.977459e-01 7.485245e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 4.708450e-01 1.222185e-01 0.2864160 3.634170e-01 4.562270e-01 5.711030e-01 7.135840e-01 ▇▆▆▅▅
numeric AF_reference 15423 0.9920738 NA NA NA NA NA NA NA 4.502871e-01 1.587661e-01 0.0001997 3.282750e-01 4.412940e-01 5.652960e-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 0.0001496 0.0001894 0.4299995 0.4296070 0.623763 0.782149 NA
1 54676 rs2462492 C T -0.0001080 0.0001876 0.5600000 0.5648736 0.400401 NA NA
1 91536 rs6702460 G T 0.0001121 0.0001847 0.5400003 0.5440047 0.456851 0.420727 NA
1 706368 rs55727773 A G -0.0002709 0.0001310 0.0389996 0.0386186 0.515650 0.275160 NA
1 763394 rs369924889 G A 0.0000748 0.0001536 0.6300007 0.6263532 0.706753 0.617612 NA
1 814495 rs74461805 C A -0.0003001 0.0001796 0.0949992 0.0947415 0.340397 NA NA
1 830181 rs28444699 A G -0.0001336 0.0001202 0.2700001 0.2661375 0.697259 0.691294 NA
1 831489 rs4970385 C T -0.0001771 0.0001180 0.1299999 0.1333260 0.705403 0.649161 NA
1 831909 rs9697642 C T -0.0001741 0.0001180 0.1400000 0.1399860 0.705448 0.648562 NA
1 832066 rs9697380 G C -0.0001754 0.0001180 0.1400000 0.1372106 0.705634 0.664337 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51164115 rs5770996 C T 0.0000354 0.0001077 0.7400005 0.7426225 0.456928 0.514776 NA
22 51164287 rs6009957 T C -0.0000305 0.0001158 0.7899998 0.7921186 0.306554 0.415535 NA
22 51165664 rs8137951 G A -0.0000345 0.0001161 0.7700005 0.7665401 0.301558 0.406350 NA
22 51174048 rs9628245 G C 0.0001053 0.0001217 0.3900004 0.3869444 0.380130 0.433107 NA
22 51181919 rs9616825 G C -0.0000405 0.0001227 0.7400005 0.7411102 0.695471 0.619409 NA
22 51186143 rs2879914 T C 0.0001681 0.0001147 0.1400000 0.1427331 0.381826 0.273363 NA
22 51186228 rs3865766 C T 0.0001480 0.0001118 0.1900002 0.1855349 0.451063 0.453275 NA
22 51197266 rs61290853 A G 0.0002803 0.0001154 0.0150000 0.0151623 0.386333 0.422923 NA
22 51212875 rs2238837 A C 0.0002099 0.0001231 0.0879995 0.0881133 0.331455 0.372404 NA
22 51237063 rs3896457 T C 0.0002801 0.0001260 0.0259998 0.0262150 0.297971 0.205072 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623763 ES:SE:LP:AF:ID  0.000149594:0.000189392:0.366532:0.623763:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  -0.000108002:0.000187628:0.251812:0.400401:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.456851 ES:SE:LP:AF:ID  0.000112098:0.000184746:0.267606:0.456851:rs6702460
1   706368  rs12029736  A   G   .   PASS    AF=0.51565  ES:SE:LP:AF:ID  -0.000270942:0.000131002:1.40894:0.51565:rs12029736
1   763394  rs3115847   G   A   .   PASS    AF=0.706753 ES:SE:LP:AF:ID  7.47763e-05:0.000153587:0.200659:0.706753:rs3115847
1   814495  rs74461805  C   A   .   PASS    AF=0.340397 ES:SE:LP:AF:ID  -0.000300142:0.000179629:1.02228:0.340397:rs74461805
1   830181  rs28444699  A   G   .   PASS    AF=0.697259 ES:SE:LP:AF:ID  -0.000133637:0.000120177:0.568636:0.697259:rs28444699
1   831489  rs4970385   C   T   .   PASS    AF=0.705403 ES:SE:LP:AF:ID  -0.000177136:0.000118003:0.886057:0.705403:rs4970385
1   831909  rs9697642   C   T   .   PASS    AF=0.705448 ES:SE:LP:AF:ID  -0.000174148:0.000117999:0.853872:0.705448:rs9697642
1   832066  rs9697380   G   C   .   PASS    AF=0.705634 ES:SE:LP:AF:ID  -0.000175386:0.000118005:0.853872:0.705634:rs9697380
1   832318  rs4500250   C   A   .   PASS    AF=0.705662 ES:SE:LP:AF:ID  -0.000176111:0.000118017:0.853872:0.705662:rs4500250
1   832918  rs28765502  T   C   .   PASS    AF=0.294371 ES:SE:LP:AF:ID  0.000175462:0.000118012:0.853872:0.294371:rs28765502
1   840753  rs4970382   T   C   .   PASS    AF=0.400106 ES:SE:LP:AF:ID  3.60489e-05:0.000108848:0.130768:0.400106:rs4970382
1   843405  rs11516185  A   G   .   PASS    AF=0.362599 ES:SE:LP:AF:ID  -2.91676e-05:0.000135123:0.0809219:0.362599:rs11516185
1   850218  rs6664536   T   A   .   PASS    AF=0.590333 ES:SE:LP:AF:ID  -5.84217e-05:0.000108529:0.229148:0.590333:rs6664536
1   850371  rs6679046   G   T   .   PASS    AF=0.603726 ES:SE:LP:AF:ID  -5.04709e-05:0.000109139:0.19382:0.603726:rs6679046
1   850780  rs6657440   C   T   .   PASS    AF=0.603944 ES:SE:LP:AF:ID  -4.80086e-05:0.000109123:0.180456:0.603944:rs6657440
1   852037  rs4970463   G   A   .   PASS    AF=0.589688 ES:SE:LP:AF:ID  -7.45662e-05:0.000108706:0.309804:0.589688:rs4970463
1   852063  rs28436996  G   A   .   PASS    AF=0.589667 ES:SE:LP:AF:ID  -7.04e-05:0.000108657:0.283997:0.589667:rs28436996
1   852875  rs13303369  C   T   .   PASS    AF=0.607674 ES:SE:LP:AF:ID  -6.20264e-05:0.000109367:0.244125:0.607674:rs13303369
1   853954  rs1806509   C   A   .   PASS    AF=0.607833 ES:SE:LP:AF:ID  -5.82576e-05:0.000109382:0.229148:0.607833:rs1806509
1   854777  rs13303019  A   G   .   PASS    AF=0.610318 ES:SE:LP:AF:ID  -5.69405e-05:0.000109489:0.221849:0.610318:rs13303019
1   854978  rs13303057  A   C   .   PASS    AF=0.603286 ES:SE:LP:AF:ID  -5.58464e-05:0.000109165:0.21467:0.603286:rs13303057
1   855075  rs6673914   C   G   .   PASS    AF=0.610338 ES:SE:LP:AF:ID  -5.94621e-05:0.000109491:0.229148:0.610338:rs6673914
1   856099  rs28534711  T   G   .   PASS    AF=0.389935 ES:SE:LP:AF:ID  6.39287e-05:0.000109512:0.251812:0.389935:rs28534711
1   856108  rs28742275  A   G   .   PASS    AF=0.389918 ES:SE:LP:AF:ID  6.41066e-05:0.000109518:0.251812:0.389918:rs28742275
1   856476  rs4040605   A   G   .   PASS    AF=0.350351 ES:SE:LP:AF:ID  9.78799e-05:0.000112506:0.420216:0.350351:rs4040605
1   866893  rs2880024   T   C   .   PASS    AF=0.610554 ES:SE:LP:AF:ID  -3.00529e-05:0.000110106:0.107905:0.610554:rs2880024
1   868418  rs28546443  C   T   .   PASS    AF=0.297865 ES:SE:LP:AF:ID  0.000122838:0.000120976:0.508638:0.297865:rs28546443
1   870645  rs28576697  T   C   .   PASS    AF=0.291287 ES:SE:LP:AF:ID  -5.81152e-05:0.000120012:0.200659:0.291287:rs28576697
1   875770  rs4970379   A   G   .   PASS    AF=0.600084 ES:SE:LP:AF:ID  -0.000163626:0.000111022:0.853872:0.600084:rs4970379
1   881627  rs2272757   G   A   .   PASS    AF=0.65239  ES:SE:LP:AF:ID  -6.64853e-05:0.000112151:0.259637:0.65239:rs2272757
1   891059  rs13303065  C   T   .   PASS    AF=0.652428 ES:SE:LP:AF:ID  -6.19903e-05:0.000112133:0.236572:0.652428:rs13303065
1   891945  rs13303106  A   G   .   PASS    AF=0.65249  ES:SE:LP:AF:ID  -6.34732e-05:0.000112264:0.244125:0.65249:rs13303106
1   903245  rs28690976  A   G   .   PASS    AF=0.566937 ES:SE:LP:AF:ID  -0.000165801:0.000111503:0.853872:0.566937:rs28690976
1   909073  rs3892467   C   T   .   PASS    AF=0.386683 ES:SE:LP:AF:ID  -0.000307852:0.0001112:2.25181:0.386683:rs3892467
1   909238  rs3829740   G   C   .   PASS    AF=0.571412 ES:SE:LP:AF:ID  -0.000182423:0.000107693:1.04576:0.571412:rs3829740
1   910394  rs28477686  C   T   .   PASS    AF=0.324456 ES:SE:LP:AF:ID  0.000236425:0.000116728:1.36653:0.324456:rs28477686
1   912049  rs7367995   T   C   .   PASS    AF=0.58525  ES:SE:LP:AF:ID  -0.000172753:0.000108785:0.958607:0.58525:rs7367995
1   913889  rs2340596   G   A   .   PASS    AF=0.599213 ES:SE:LP:AF:ID  -0.000135826:0.000108962:0.677781:0.599213:rs2340596
1   914333  rs13302979  C   G   .   PASS    AF=0.602521 ES:SE:LP:AF:ID  -0.000141409:0.000109293:0.69897:0.602521:rs13302979
1   914852  rs13303368  G   C   .   PASS    AF=0.600078 ES:SE:LP:AF:ID  -0.000148257:0.000109083:0.769551:0.600078:rs13303368
1   914940  rs13303033  T   C   .   PASS    AF=0.584292 ES:SE:LP:AF:ID  -0.000159003:0.000108471:0.853872:0.584292:rs13303033
1   916834  rs6694632   G   A   .   PASS    AF=0.589106 ES:SE:LP:AF:ID  -0.000144749:0.000108636:0.744727:0.589106:rs6694632
1   918384  rs13303118  G   T   .   PASS    AF=0.584205 ES:SE:LP:AF:ID  -0.000136188:0.00010842:0.677781:0.584205:rs13303118
1   918573  rs2341354   A   G   .   PASS    AF=0.589331 ES:SE:LP:AF:ID  -0.00014764:0.000108553:0.769551:0.589331:rs2341354
1   919501  rs4970414   G   T   .   PASS    AF=0.583932 ES:SE:LP:AF:ID  -0.000155806:0.000112263:0.769551:0.583932:rs4970414
1   921716  rs13303278  C   A   .   PASS    AF=0.567888 ES:SE:LP:AF:ID  -0.000269066:0.00010833:1.88606:0.567888:rs13303278
1   924528  rs34712273  C   A   .   PASS    AF=0.578492 ES:SE:LP:AF:ID  -0.00025713:0.000108641:1.74473:0.578492:rs34712273
1   930533  rs3128110   C   G   .   PASS    AF=0.386078 ES:SE:LP:AF:ID  0.000350184:0.000110029:2.82391:0.386078:rs3128110