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_20002_1261.vcf.gz --id UKB-b:17670 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20002_1261.txt.gz --cohort_cases 1679 --cohort_controls 461254 --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-17670/UKB-b-17670_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17670/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17670/UKB-b-17670_data.vcf.gz ...
Read summary statistics for 2780866 SNPs.
Dropped 341 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, 697606 SNPs remain.
After merging with regression SNP LD, 697606 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0024 (0.0013)
Lambda GC: 1.0464
Mean Chi^2: 1.0531
Intercept: 1.0282 (0.0102)
Ratio: 0.5313 (0.1917)
Analysis finished at Thu Oct 17 14:40:55 2019
Total time elapsed: 36.56s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.7981,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -3.3318e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 5,
    "n_p_sig": 4851,
    "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": 22081,
    "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": 697606,
    "ldsc_nsnp_merge_regression_ld": 697606,
    "ldsc_observed_scale_h2_beta": 0.0024,
    "ldsc_observed_scale_h2_se": 0.0013,
    "ldsc_intercept_beta": 1.0282,
    "ldsc_intercept_se": 0.0102,
    "ldsc_lambda_gc": 1.0464,
    "ldsc_mean_chisq": 1.0531,
    "ldsc_ratio": 0.5311
}
 

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 TRUE
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 1.0000000 4 58 0 2780528 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 2780866 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 8.660247e+00 5.770080e+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 7.855400e+07 5.664928e+07 5687.0000000 3.169581e+07 6.896856e+07 1.147315e+08 2.492013e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -3.300000e-06 1.506000e-04 -0.0036999 -9.430000e-05 -1.100000e-06 9.180000e-05 8.617000e-04 ▁▁▁▅▇
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.347000e-04 8.900000e-06 0.0001205 1.270000e-04 1.321000e-04 1.410000e-04 2.750000e-04 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.913176e-01 2.916072e-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 4.913188e-01 2.915819e-01 0.0000000 2.362855e-01 4.886968e-01 7.438419e-01 9.999995e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 4.431900e-01 1.651686e-01 0.2084580 2.980350e-01 4.160500e-01 5.740227e-01 7.915420e-01 ▇▆▅▃▃
numeric AF_reference 22081 0.9920597 NA NA NA NA NA NA NA 4.260117e-01 1.851090e-01 0.0001997 2.783550e-01 4.063500e-01 5.619010e-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 0.0001881 0.0002218 0.4000000 0.3965371 0.623763 0.7821490 NA
1 54676 rs2462492 C T -0.0002041 0.0002198 0.3500000 0.3531229 0.400401 NA NA
1 91536 rs6702460 G T -0.0003414 0.0002164 0.1100001 0.1146596 0.456851 0.4207270 NA
1 534192 rs6680723 C T -0.0003002 0.0002472 0.2200002 0.2245150 0.240960 NA NA
1 706368 rs55727773 A G 0.0000744 0.0001534 0.6300007 0.6277601 0.515650 0.2751600 NA
1 763394 rs369924889 G A 0.0003119 0.0001799 0.0830004 0.0830025 0.706753 0.6176120 NA
1 768253 rs2977608 A C 0.0000726 0.0001468 0.6200004 0.6209956 0.761304 0.4894170 NA
1 776546 rs12124819 A G -0.0000592 0.0001640 0.7199992 0.7182555 0.265390 0.0756789 NA
1 808631 rs11240779 G A 0.0001391 0.0001491 0.3500000 0.3509143 0.772626 0.4534740 NA
1 808928 rs11240780 C T 0.0001353 0.0001493 0.3599996 0.3649349 0.772854 0.4522760 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51180501 rs5770999 T C -0.0001398 0.0001444 0.3300000 0.3330291 0.713658 0.6369810 NA
22 51181919 rs9616825 G C -0.0001299 0.0001437 0.3700002 0.3658796 0.695471 0.6194090 NA
22 51182485 rs6009961 A G -0.0000976 0.0001449 0.5000000 0.5006989 0.715505 0.6383790 NA
22 51186143 rs2879914 T C -0.0000103 0.0001343 0.9400001 0.9390026 0.381826 0.2733630 NA
22 51186228 rs3865766 C T -0.0001180 0.0001309 0.3700002 0.3676307 0.451063 0.4532750 NA
22 51197266 rs61290853 A G -0.0001818 0.0001352 0.1800002 0.1785770 0.386333 0.4229230 NA
22 51198027 rs34939255 A G 0.0000015 0.0001530 0.9900000 0.9919606 0.254557 0.0984425 NA
22 51211106 rs9628250 T C 0.0000164 0.0001517 0.9100000 0.9137080 0.271547 0.1671330 NA
22 51212875 rs2238837 A C -0.0000398 0.0001442 0.7800007 0.7827073 0.331455 0.3724040 NA
22 51237063 rs3896457 T C -0.0000426 0.0001476 0.7700005 0.7729534 0.297971 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623763 ES:SE:LP:AF:ID  0.00018808:0.000221839:0.39794:0.623763:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  -0.00020407:0.000219773:0.455932:0.400401:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.456851 ES:SE:LP:AF:ID  -0.000341386:0.000216397:0.958607:0.456851:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.24096  ES:SE:LP:AF:ID  -0.000300226:0.000247179:0.657577:0.24096:rs6680723
1   706368  rs12029736  A   G   .   PASS    AF=0.51565  ES:SE:LP:AF:ID  7.44032e-05:0.000153446:0.200659:0.51565:rs12029736
1   763394  rs3115847   G   A   .   PASS    AF=0.706753 ES:SE:LP:AF:ID  0.000311861:0.0001799:1.08092:0.706753:rs3115847
1   768253  rs2977608   A   C   .   PASS    AF=0.761304 ES:SE:LP:AF:ID  7.25991e-05:0.000146831:0.207608:0.761304:rs2977608
1   776546  rs12124819  A   G   .   PASS    AF=0.26539  ES:SE:LP:AF:ID  -5.91741e-05:0.000164012:0.142668:0.26539:rs12124819
1   808631  rs11240779  G   A   .   PASS    AF=0.772626 ES:SE:LP:AF:ID  0.000139056:0.000149071:0.455932:0.772626:rs11240779
1   808928  rs11240780  C   T   .   PASS    AF=0.772854 ES:SE:LP:AF:ID  0.000135286:0.000149322:0.443698:0.772854:rs11240780
1   814495  rs74461805  C   A   .   PASS    AF=0.340397 ES:SE:LP:AF:ID  -0.000136716:0.000210404:0.283997:0.340397:rs74461805
1   830181  rs28444699  A   G   .   PASS    AF=0.697259 ES:SE:LP:AF:ID  -8.78989e-05:0.000140766:0.275724:0.697259:rs28444699
1   831489  rs4970385   C   T   .   PASS    AF=0.705403 ES:SE:LP:AF:ID  -0.000209151:0.00013822:0.886057:0.705403:rs4970385
1   831909  rs9697642   C   T   .   PASS    AF=0.705448 ES:SE:LP:AF:ID  -0.000210033:0.000138215:0.886057:0.705448:rs9697642
1   832066  rs9697380   G   C   .   PASS    AF=0.705634 ES:SE:LP:AF:ID  -0.000212342:0.000138221:0.920819:0.705634:rs9697380
1   832318  rs4500250   C   A   .   PASS    AF=0.705662 ES:SE:LP:AF:ID  -0.000212791:0.000138236:0.920819:0.705662:rs4500250
1   832398  rs4553118   T   C   .   PASS    AF=0.730134 ES:SE:LP:AF:ID  -0.000119721:0.000142003:0.39794:0.730134:rs4553118
1   832918  rs28765502  T   C   .   PASS    AF=0.294371 ES:SE:LP:AF:ID  0.000212016:0.00013823:0.886057:0.294371:rs28765502
1   833223  rs13303211  C   T   .   PASS    AF=0.236696 ES:SE:LP:AF:ID  0.000303687:0.000147167:1.40894:0.236696:rs13303211
1   833302  rs28752186  C   T   .   PASS    AF=0.236684 ES:SE:LP:AF:ID  0.000303952:0.000147169:1.40894:0.236684:rs28752186
1   833641  rs28594623  T   C   .   PASS    AF=0.239743 ES:SE:LP:AF:ID  0.00030896:0.000146697:1.45593:0.239743:rs28594623
1   833824  rs28484835  T   C   .   PASS    AF=0.236686 ES:SE:LP:AF:ID  0.000303818:0.000147168:1.40894:0.236686:rs28484835
1   833927  rs28593608  T   C   .   PASS    AF=0.212416 ES:SE:LP:AF:ID  0.000208143:0.000152961:0.769551:0.212416:rs28593608
1   834198  rs28385272  T   C   .   PASS    AF=0.212311 ES:SE:LP:AF:ID  0.000210795:0.000152988:0.769551:0.212311:rs28385272
1   834832  rs4411087   G   C   .   PASS    AF=0.237171 ES:SE:LP:AF:ID  0.000296958:0.000147055:1.36653:0.237171:rs4411087
1   834928  rs4422949   A   G   .   PASS    AF=0.212956 ES:SE:LP:AF:ID  0.000212292:0.00015277:0.79588:0.212956:rs4422949
1   834999  rs28570054  G   A   .   PASS    AF=0.212918 ES:SE:LP:AF:ID  0.000212457:0.000152801:0.79588:0.212918:rs28570054
1   835499  rs4422948   A   G   .   PASS    AF=0.241155 ES:SE:LP:AF:ID  0.000275667:0.000146028:1.22915:0.241155:rs4422948
1   836529  rs28731045  C   G   .   PASS    AF=0.213538 ES:SE:LP:AF:ID  0.000202749:0.000152576:0.744727:0.213538:rs28731045
1   836896  rs28705752  T   C   .   PASS    AF=0.269503 ES:SE:LP:AF:ID  0.000210251:0.000140906:0.853872:0.269503:rs28705752
1   836924  rs72890788  G   A   .   PASS    AF=0.213508 ES:SE:LP:AF:ID  0.000203333:0.000152595:0.744727:0.213508:rs72890788
1   838387  rs4970384   T   C   .   PASS    AF=0.214583 ES:SE:LP:AF:ID  0.000221669:0.000152301:0.823909:0.214583:rs4970384
1   838555  rs4970383   C   A   .   PASS    AF=0.246197 ES:SE:LP:AF:ID  0.000137831:0.000145025:0.468521:0.246197:rs4970383
1   839103  rs28562941  A   G   .   PASS    AF=0.27001  ES:SE:LP:AF:ID  0.000204448:0.000141006:0.823909:0.27001:rs28562941
1   840753  rs4970382   T   C   .   PASS    AF=0.400106 ES:SE:LP:AF:ID  0.000145267:0.000127496:0.60206:0.400106:rs4970382
1   841085  rs1574243   C   G   .   PASS    AF=0.237094 ES:SE:LP:AF:ID  6.92346e-05:0.000148093:0.19382:0.237094:rs1574243
1   842013  rs7419119   T   G   .   PASS    AF=0.215384 ES:SE:LP:AF:ID  0.000213425:0.000152403:0.79588:0.215384:rs7419119
1   842362  rs28540380  C   T   .   PASS    AF=0.235321 ES:SE:LP:AF:ID  0.000267825:0.000150308:1.12494:0.235321:rs28540380
1   843405  rs11516185  A   G   .   PASS    AF=0.362599 ES:SE:LP:AF:ID  5.78633e-05:0.000158273:0.148742:0.362599:rs11516185
1   845938  rs57760052  G   A   .   PASS    AF=0.210865 ES:SE:LP:AF:ID  0.000110983:0.000153503:0.327902:0.210865:rs57760052
1   847491  rs28407778  G   A   .   PASS    AF=0.2142   ES:SE:LP:AF:ID  0.000103733:0.000152478:0.30103:0.2142:rs28407778
1   848090  rs4246505   G   A   .   PASS    AF=0.212516 ES:SE:LP:AF:ID  0.000104145:0.000152879:0.30103:0.212516:rs4246505
1   848445  rs4626817   G   A   .   PASS    AF=0.209298 ES:SE:LP:AF:ID  0.000112746:0.000154386:0.327902:0.209298:rs4626817
1   848456  rs11507767  A   G   .   PASS    AF=0.209247 ES:SE:LP:AF:ID  0.000113825:0.000154414:0.337242:0.209247:rs11507767
1   848738  rs3829741   C   T   .   PASS    AF=0.21234  ES:SE:LP:AF:ID  0.000105175:0.000153017:0.309804:0.21234:rs3829741
1   850062  rs28723578  A   T   .   PASS    AF=0.214409 ES:SE:LP:AF:ID  0.000116913:0.000152337:0.356547:0.214409:rs28723578
1   850123  rs28622257  C   T   .   PASS    AF=0.212775 ES:SE:LP:AF:ID  0.000113008:0.000152725:0.337242:0.212775:rs28622257
1   850218  rs6664536   T   A   .   PASS    AF=0.590333 ES:SE:LP:AF:ID  2.86321e-05:0.000127123:0.0861861:0.590333:rs6664536
1   850371  rs6679046   G   T   .   PASS    AF=0.603726 ES:SE:LP:AF:ID  -5.66756e-06:0.000127836:0.0177288:0.603726:rs6679046
1   850780  rs6657440   C   T   .   PASS    AF=0.603944 ES:SE:LP:AF:ID  -7.1927e-06:0.000127818:0.0177288:0.603944:rs6657440