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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    "FORMAT.1": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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    "FORMAT.5": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
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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_20008.vcf.gz --id UKB-b:17841 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20008.txt.gz --cohort_controls 342336 --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-17841/UKB-b-17841_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17841/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:28 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-17841/UKB-b-17841_data.vcf.gz ...
Read summary statistics for 9838392 SNPs.
Dropped 14581 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, 1289124 SNPs remain.
After merging with regression SNP LD, 1289124 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0233 (0.0019)
Lambda GC: 1.1525
Mean Chi^2: 1.1779
Intercept: 1.0208 (0.0076)
Ratio: 0.1168 (0.0427)
Analysis finished at Thu Oct 17 14:42:15 2019
Total time elapsed: 1.0m:46.26s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9499,
    "inflation_factor": 1.0966,
    "mean_EFFECT": 0.0002,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 19,
    "n_p_sig": 1033,
    "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": 184257,
    "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": 1289124,
    "ldsc_nsnp_merge_regression_ld": 1289124,
    "ldsc_observed_scale_h2_beta": 0.0233,
    "ldsc_observed_scale_h2_se": 0.0019,
    "ldsc_intercept_beta": 1.0208,
    "ldsc_intercept_se": 0.0076,
    "ldsc_lambda_gc": 1.1525,
    "ldsc_mean_chisq": 1.1779,
    "ldsc_ratio": 0.1169
}

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 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 9823879 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 9838392 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 8.622626e+00 5.748265e+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 7.886175e+07 5.628386e+07 828.0000000 3.259055e+07 6.949499e+07 1.145941e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.952000e-04 1.163920e-02 -0.2090000 -3.523800e-03 5.310000e-05 3.693100e-03 2.066740e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 8.280800e-03 7.802700e-03 0.0023276 2.849300e-03 4.771200e-03 1.098960e-02 1.221520e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.829427e-01 2.934123e-01 0.0000000 2.200002e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.829446e-01 2.933852e-01 0.0000000 2.248691e-01 4.769206e-01 7.369843e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.037148e-01 2.568540e-01 0.0010230 1.325600e-02 7.825300e-02 3.168660e-01 9.989770e-01 ▇▂▁▁▁
numeric AF_reference 184257 0.9812716 NA NA NA NA NA NA NA 2.069881e-01 2.483212e-01 0.0000000 1.198080e-02 1.002400e-01 3.206870e-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 -0.0078567 0.0042841 0.0669993 0.0666621 0.623797 0.7821490 NA
1 54676 rs2462492 C T -0.0005310 0.0042447 0.9000000 0.9004407 0.400325 NA NA
1 86028 rs114608975 T C 0.0126957 0.0067854 0.0610000 0.0613399 0.103579 0.0277556 NA
1 91536 rs6702460 G T -0.0003577 0.0041780 0.9299999 0.9317710 0.456767 0.4207270 NA
1 234313 rs8179466 C T -0.0181739 0.0082189 0.0269998 0.0270199 0.074632 NA NA
1 534192 rs6680723 C T 0.0042337 0.0047770 0.3800004 0.3754740 0.240780 NA NA
1 546697 rs12025928 A G 0.0029834 0.0059433 0.6200004 0.6156902 0.913282 NA NA
1 693731 rs12238997 A G 0.0004336 0.0040015 0.9100000 0.9137147 0.116314 0.1417730 NA
1 705882 rs72631875 G A -0.0042752 0.0058566 0.4700002 0.4654028 0.067405 0.0315495 NA
1 706368 rs55727773 A G -0.0020574 0.0029616 0.4899999 0.4872597 0.515508 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0014826 0.0062135 0.8100000 0.8114065 0.042008 0.0473243 NA
22 51219766 rs182321900 C T 0.0032100 0.0291021 0.9100000 0.9121704 0.001923 NA NA
22 51220146 rs868950473 C T 0.0150671 0.0288460 0.5999997 0.6014413 0.001968 NA NA
22 51221190 rs369304721 G A 0.0025239 0.0062157 0.6800001 0.6847024 0.049687 NA NA
22 51221731 rs115055839 T C -0.0017860 0.0046468 0.6999999 0.7007092 0.073206 0.0625000 NA
22 51222100 rs114553188 G T -0.0029820 0.0054566 0.5800000 0.5847256 0.054677 0.0880591 NA
22 51223637 rs375798137 G A -0.0029177 0.0054828 0.5900000 0.5946250 0.054304 0.0788738 NA
22 51229805 rs9616985 T C -0.0013132 0.0046637 0.7800007 0.7782701 0.073039 0.0730831 NA
22 51232488 rs376461333 A G -0.0033409 0.0109366 0.7600007 0.7600029 0.020190 NA NA
22 51237063 rs3896457 T C 0.0063416 0.0028478 0.0259998 0.0259588 0.298539 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623797 ES:SE:LP:AF:ID  -0.00785675:0.00428407:1.17393:0.623797:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400325 ES:SE:LP:AF:ID  -0.000531034:0.00424472:0.0457575:0.400325:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103579 ES:SE:LP:AF:ID  0.0126957:0.00678538:1.21467:0.103579:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456767 ES:SE:LP:AF:ID  -0.00035771:0.00417803:0.0315171:0.456767:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074632 ES:SE:LP:AF:ID  -0.0181739:0.00821891:1.56864:0.074632:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24078  ES:SE:LP:AF:ID  0.00423373:0.00477704:0.420216:0.24078:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913282 ES:SE:LP:AF:ID  0.00298336:0.00594333:0.207608:0.913282:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116314 ES:SE:LP:AF:ID  0.000433575:0.00400146:0.0409586:0.116314:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067405 ES:SE:LP:AF:ID  -0.00427519:0.00585659:0.327902:0.067405:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515508 ES:SE:LP:AF:ID  -0.00205736:0.00296162:0.309804:0.515508:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032828 ES:SE:LP:AF:ID  0.00870114:0.00748821:0.60206:0.032828:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036418 ES:SE:LP:AF:ID  0.00766398:0.00680287:0.585027:0.036418:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.03654  ES:SE:LP:AF:ID  0.00799279:0.00677638:0.619789:0.03654:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036225 ES:SE:LP:AF:ID  0.0071981:0.00682753:0.537602:0.036225:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016518 ES:SE:LP:AF:ID  -0.010994:0.0104389:0.537602:0.016518:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036783 ES:SE:LP:AF:ID  0.00800193:0.00674891:0.619789:0.036783:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036868 ES:SE:LP:AF:ID  0.00825865:0.00672718:0.657577:0.036868:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101278 ES:SE:LP:AF:ID  -0.000115184:0.00488443:0.00877392:0.101278:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959331 ES:SE:LP:AF:ID  -0.00794409:0.0064912:0.657577:0.959331:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031453 ES:SE:LP:AF:ID  0.00275471:0.0117562:0.091515:0.031453:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053168 ES:SE:LP:AF:ID  -0.000142578:0.00936629:0.00436481:0.053168:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036378 ES:SE:LP:AF:ID  0.00786597:0.00677128:0.60206:0.036378:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036708 ES:SE:LP:AF:ID  0.00734283:0.00670929:0.568636:0.036708:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843456 ES:SE:LP:AF:ID  -0.00311585:0.0034699:0.431798:0.843456:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055831 ES:SE:LP:AF:ID  0.00135566:0.00561773:0.091515:0.055831:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122286 ES:SE:LP:AF:ID  -0.000104879:0.00379601:0.00877392:0.122286:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025814 ES:SE:LP:AF:ID  -0.00759665:0.00931163:0.387216:0.025814:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121538 ES:SE:LP:AF:ID  -0.000221049:0.00379753:0.0222764:0.121538:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.131998 ES:SE:LP:AF:ID  0.0038609:0.00374732:0.522879:0.131998:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.01112  ES:SE:LP:AF:ID  0.00289698:0.0136019:0.0809219:0.01112:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005743 ES:SE:LP:AF:ID  0.01523:0.017495:0.420216:0.005743:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002221 ES:SE:LP:AF:ID  0.0466966:0.0300034:0.920819:0.002221:rs112573343
1   752478  rs146277091 G   A   .   PASS    AF=0.036621 ES:SE:LP:AF:ID  0.00772992:0.00664114:0.619789:0.036621:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.83911  ES:SE:LP:AF:ID  -0.0020275:0.00336081:0.259637:0.83911:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83874  ES:SE:LP:AF:ID  -0.00188883:0.00335707:0.244125:0.83874:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869736 ES:SE:LP:AF:ID  0.000556204:0.00360077:0.0555173:0.869736:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129943 ES:SE:LP:AF:ID  -0.000772577:0.00360792:0.0809219:0.129943:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037121 ES:SE:LP:AF:ID  0.00888993:0.00652975:0.769551:0.037121:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037373 ES:SE:LP:AF:ID  0.00901597:0.00648786:0.79588:0.037373:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869074 ES:SE:LP:AF:ID  0.000667628:0.00359359:0.0705811:0.869074:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869172 ES:SE:LP:AF:ID  0.000764274:0.00359501:0.0809219:0.869172:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037321 ES:SE:LP:AF:ID  0.00914949:0.00651665:0.79588:0.037321:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869079 ES:SE:LP:AF:ID  0.000600548:0.00359357:0.0604807:0.869079:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005139 ES:SE:LP:AF:ID  -0.00010793:0.0184081:-0:0.005139:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005104 ES:SE:LP:AF:ID  -0.000181728:0.0184581:0.00436481:0.005104:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.8382   ES:SE:LP:AF:ID  -0.00184486:0.00334803:0.236572:0.8382:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037333 ES:SE:LP:AF:ID  0.0087641:0.00652627:0.744727:0.037333:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838824 ES:SE:LP:AF:ID  -0.00181026:0.00335737:0.229148:0.838824:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013847 ES:SE:LP:AF:ID  0.00756797:0.0116721:0.283997:0.013847:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005575 ES:SE:LP:AF:ID  0.00756772:0.0180301:0.173925:0.005575:rs184270342