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_20088_333.vcf.gz --id UKB-b:5850 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20088_333.txt.gz --cohort_cases 2142 --cohort_controls 62807 --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-5850/UKB-b-5850_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5850/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:41 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5850/UKB-b-5850_data.vcf.gz ...
Read summary statistics for 3340177 SNPs.
Dropped 494 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, 821284 SNPs remain.
After merging with regression SNP LD, 821284 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0154 (0.0082)
Lambda GC: 1.0228
Mean Chi^2: 1.0221
Intercept: 0.9996 (0.0079)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:46:19 2019
Total time elapsed: 38.77s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.8417,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 3.7039e-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": 26786,
    "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": 821284,
    "ldsc_nsnp_merge_regression_ld": 821284,
    "ldsc_observed_scale_h2_beta": 0.0154,
    "ldsc_observed_scale_h2_se": 0.0082,
    "ldsc_intercept_beta": 0.9996,
    "ldsc_intercept_se": 0.0079,
    "ldsc_lambda_gc": 1.0228,
    "ldsc_mean_chisq": 1.0221,
    "ldsc_ratio": -0.0181
}
 

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 3339686 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 3340177 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 8.662550e+00 5.773079e+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 7.854526e+07 5.672515e+07 828.0000000 3.157508e+07 6.892083e+07 1.147880e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 4.000000e-07 1.122900e-03 -0.0073362 -7.521000e-04 -7.000000e-07 7.470000e-04 6.090100e-03 ▁▁▇▃▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.103800e-03 1.043000e-04 0.0009564 1.013000e-03 1.071300e-03 1.177600e-03 2.374800e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.957610e-01 2.901233e-01 0.0000004 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.957633e-01 2.900975e-01 0.0000004 2.427451e-01 4.944877e-01 7.470554e-01 9.999994e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 4.217460e-01 1.893748e-01 0.1633990 2.556830e-01 3.856590e-01 5.672700e-01 8.366010e-01 ▇▆▅▃▂
numeric AF_reference 26786 0.9919807 NA NA NA NA NA NA NA 4.073631e-01 2.008672e-01 0.0000000 2.442090e-01 3.783950e-01 5.545130e-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 -0.0004680 0.0017626 0.7899998 0.7906293 0.623812 0.782149 NA
1 54676 rs2462492 C T -0.0009215 0.0017574 0.5999997 0.6000302 0.399144 NA NA
1 91536 rs6702460 G T 0.0021901 0.0017286 0.2099999 0.2051487 0.455916 0.420727 NA
1 534192 rs6680723 C T 0.0014907 0.0019688 0.4500005 0.4489536 0.242057 NA NA
1 706368 rs55727773 A G -0.0026818 0.0012185 0.0280001 0.0277424 0.513304 0.275160 NA
1 754503 rs3115859 G A -0.0009965 0.0013684 0.4700002 0.4664659 0.836159 0.663938 NA
1 760912 rs1048488 C T -0.0011129 0.0013649 0.4100001 0.4148744 0.836369 0.734026 NA
1 761147 rs3115850 T C -0.0011214 0.0013659 0.4100001 0.4116509 0.836495 0.733427 NA
1 763394 rs369924889 G A 0.0025036 0.0014276 0.0790005 0.0794644 0.705804 0.617612 NA
1 768253 rs2977608 A C -0.0011016 0.0011570 0.3400001 0.3410409 0.758252 0.489417 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51182485 rs6009961 A G -0.0010001 0.0011456 0.3800004 0.3826774 0.714237 0.6383790 NA
22 51186143 rs2879914 T C -0.0023039 0.0010669 0.0309999 0.0308150 0.380077 0.2733630 NA
22 51186228 rs3865766 C T -0.0018111 0.0010392 0.0810009 0.0813686 0.449547 0.4532750 NA
22 51192586 rs5771006 G A -0.0016765 0.0014010 0.2300001 0.2314438 0.167396 0.0848642 NA
22 51193227 rs34608236 T G 0.0001397 0.0014287 0.9199999 0.9221188 0.168560 0.0692891 NA
22 51197266 rs61290853 A G -0.0015919 0.0010709 0.1400000 0.1371513 0.386693 0.4229230 NA
22 51198027 rs34939255 A G 0.0011829 0.0012146 0.3300000 0.3300898 0.254586 0.0984425 NA
22 51211106 rs9628250 T C 0.0012772 0.0012048 0.2900000 0.2890950 0.271468 0.1671330 NA
22 51212875 rs2238837 A C -0.0025415 0.0011453 0.0259998 0.0264767 0.331351 0.3724040 NA
22 51237063 rs3896457 T C -0.0019452 0.0011700 0.0959997 0.0964037 0.298393 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623812 ES:SE:LP:AF:ID  -0.000467951:0.00176257:0.102373:0.623812:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399144 ES:SE:LP:AF:ID  -0.000921526:0.00175744:0.221849:0.399144:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.455916 ES:SE:LP:AF:ID  0.00219013:0.00172857:0.677781:0.455916:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.242057 ES:SE:LP:AF:ID  0.00149066:0.00196875:0.346787:0.242057:rs6680723
1   706368  rs12029736  A   G   .   PASS    AF=0.513304 ES:SE:LP:AF:ID  -0.00268183:0.00121851:1.55284:0.513304:rs12029736
1   754503  rs3115859   G   A   .   PASS    AF=0.836159 ES:SE:LP:AF:ID  -0.000996489:0.00136835:0.327902:0.836159:rs3115859
1   760912  rs1048488   C   T   .   PASS    AF=0.836369 ES:SE:LP:AF:ID  -0.00111289:0.00136493:0.387216:0.836369:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.836495 ES:SE:LP:AF:ID  -0.00112136:0.00136586:0.387216:0.836495:rs3115850
1   763394  rs3115847   G   A   .   PASS    AF=0.705804 ES:SE:LP:AF:ID  0.00250364:0.00142755:1.10237:0.705804:rs3115847
1   768253  rs2977608   A   C   .   PASS    AF=0.758252 ES:SE:LP:AF:ID  -0.00110163:0.00115704:0.468521:0.758252:rs2977608
1   776546  rs12124819  A   G   .   PASS    AF=0.263729 ES:SE:LP:AF:ID  -0.000126435:0.00130741:0.0362122:0.263729:rs12124819
1   798400  rs10900604  A   G   .   PASS    AF=0.209824 ES:SE:LP:AF:ID  0.00129647:0.00123354:0.537602:0.209824:rs10900604
1   798959  rs11240777  G   A   .   PASS    AF=0.209642 ES:SE:LP:AF:ID  0.00130512:0.0012342:0.537602:0.209642:rs11240777
1   808631  rs11240779  G   A   .   PASS    AF=0.76828  ES:SE:LP:AF:ID  -0.00204491:0.001173:1.09151:0.76828:rs11240779
1   808928  rs11240780  C   T   .   PASS    AF=0.768417 ES:SE:LP:AF:ID  -0.00205924:0.0011745:1.09691:0.768417:rs11240780
1   814495  rs74461805  C   A   .   PASS    AF=0.340108 ES:SE:LP:AF:ID  -0.000669305:0.00166862:0.161151:0.340108:rs74461805
1   830181  rs28444699  A   G   .   PASS    AF=0.696612 ES:SE:LP:AF:ID  -0.00027543:0.00111635:0.091515:0.696612:rs28444699
1   831489  rs4970385   C   T   .   PASS    AF=0.705031 ES:SE:LP:AF:ID  -0.000199761:0.00109613:0.0655015:0.705031:rs4970385
1   831909  rs9697642   C   T   .   PASS    AF=0.705083 ES:SE:LP:AF:ID  -0.000186914:0.00109613:0.0655015:0.705083:rs9697642
1   832066  rs9697380   G   C   .   PASS    AF=0.705261 ES:SE:LP:AF:ID  -0.000203737:0.00109608:0.0705811:0.705261:rs9697380
1   832318  rs4500250   C   A   .   PASS    AF=0.705286 ES:SE:LP:AF:ID  -0.000207337:0.00109623:0.0705811:0.705286:rs4500250
1   832398  rs4553118   T   C   .   PASS    AF=0.730154 ES:SE:LP:AF:ID  3.77531e-05:0.00112647:0.0132283:0.730154:rs4553118
1   832918  rs28765502  T   C   .   PASS    AF=0.294729 ES:SE:LP:AF:ID  0.000205712:0.00109619:0.0705811:0.294729:rs28765502
1   833223  rs13303211  C   T   .   PASS    AF=0.237157 ES:SE:LP:AF:ID  0.000166589:0.00116703:0.05061:0.237157:rs13303211
1   833302  rs28752186  C   T   .   PASS    AF=0.237168 ES:SE:LP:AF:ID  0.000164811:0.00116705:0.05061:0.237168:rs28752186
1   833641  rs28594623  T   C   .   PASS    AF=0.240204 ES:SE:LP:AF:ID  7.04165e-05:0.00116358:0.0222764:0.240204:rs28594623
1   833824  rs28484835  T   C   .   PASS    AF=0.23716  ES:SE:LP:AF:ID  0.000164564:0.0011671:0.05061:0.23716:rs28484835
1   833927  rs28593608  T   C   .   PASS    AF=0.212467 ES:SE:LP:AF:ID  -0.000143618:0.00121319:0.0409586:0.212467:rs28593608
1   834198  rs28385272  T   C   .   PASS    AF=0.21236  ES:SE:LP:AF:ID  -0.000119991:0.0012135:0.0362122:0.21236:rs28385272
1   834832  rs4411087   G   C   .   PASS    AF=0.237633 ES:SE:LP:AF:ID  0.000159276:0.00116622:0.05061:0.237633:rs4411087
1   834928  rs4422949   A   G   .   PASS    AF=0.213048 ES:SE:LP:AF:ID  -0.000122994:0.00121138:0.0362122:0.213048:rs4422949
1   834999  rs28570054  G   A   .   PASS    AF=0.213007 ES:SE:LP:AF:ID  -0.000116319:0.00121161:0.0362122:0.213007:rs28570054
1   835499  rs4422948   A   G   .   PASS    AF=0.241744 ES:SE:LP:AF:ID  -7.41082e-05:0.0011575:0.0222764:0.241744:rs4422948
1   836529  rs28731045  C   G   .   PASS    AF=0.213504 ES:SE:LP:AF:ID  -9.73583e-05:0.00121016:0.0268721:0.213504:rs28731045
1   836896  rs28705752  T   C   .   PASS    AF=0.269687 ES:SE:LP:AF:ID  0.000842271:0.00111845:0.346787:0.269687:rs28705752
1   836924  rs72890788  G   A   .   PASS    AF=0.213501 ES:SE:LP:AF:ID  -1.68166e-05:0.00121031:0.00436481:0.213501:rs72890788
1   838387  rs4970384   T   C   .   PASS    AF=0.214599 ES:SE:LP:AF:ID  7.77648e-05:0.00120797:0.0222764:0.214599:rs4970384
1   838555  rs4970383   C   A   .   PASS    AF=0.245985 ES:SE:LP:AF:ID  0.000884119:0.00115145:0.356547:0.245985:rs4970383
1   839103  rs28562941  A   G   .   PASS    AF=0.270067 ES:SE:LP:AF:ID  0.000846589:0.00111937:0.346787:0.270067:rs28562941
1   840753  rs4970382   T   C   .   PASS    AF=0.400406 ES:SE:LP:AF:ID  -0.00105226:0.0010119:0.522879:0.400406:rs4970382
1   841085  rs1574243   C   G   .   PASS    AF=0.236925 ES:SE:LP:AF:ID  0.00141919:0.00117637:0.638272:0.236925:rs1574243
1   842013  rs7419119   T   G   .   PASS    AF=0.215763 ES:SE:LP:AF:ID  0.000202884:0.00120887:0.0604807:0.215763:rs7419119
1   842362  rs28540380  C   T   .   PASS    AF=0.234687 ES:SE:LP:AF:ID  -0.000712908:0.00119479:0.259637:0.234687:rs28540380
1   843405  rs11516185  A   G   .   PASS    AF=0.362367 ES:SE:LP:AF:ID  -0.000756793:0.00125897:0.259637:0.362367:rs11516185
1   844300  rs61769713  C   G   .   PASS    AF=0.819531 ES:SE:LP:AF:ID  0.00125159:0.00128787:0.481486:0.819531:rs61769713
1   845283  rs7366404   G   T   .   PASS    AF=0.815154 ES:SE:LP:AF:ID  0.000878878:0.00127672:0.309804:0.815154:rs7366404
1   845635  rs117086422 C   T   .   PASS    AF=0.206525 ES:SE:LP:AF:ID  -0.000207741:0.00122583:0.0604807:0.206525:rs117086422
1   845938  rs57760052  G   A   .   PASS    AF=0.212239 ES:SE:LP:AF:ID  3.15551e-05:0.00121413:0.00877392:0.212239:rs57760052
1   846078  rs28612348  C   T   .   PASS    AF=0.197605 ES:SE:LP:AF:ID  -0.000629102:0.00124429:0.21467:0.197605:rs28612348
1   846338  rs4970334   A   G   .   PASS    AF=0.814547 ES:SE:LP:AF:ID  0.000937552:0.00127589:0.337242:0.814547:rs4970334