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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_20011.vcf.gz --id UKB-b:5666 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20011.txt.gz --cohort_controls 364083 --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-5666/UKB-b-5666_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5666/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:32 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5666/UKB-b-5666_data.vcf.gz ...
Read summary statistics for 9851713 SNPs.
Dropped 14738 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, 1289166 SNPs remain.
After merging with regression SNP LD, 1289166 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0238 (0.002)
Lambda GC: 1.1806
Mean Chi^2: 1.2046
Intercept: 1.0336 (0.0072)
Ratio: 0.1643 (0.0349)
Analysis finished at Thu Oct 17 14:47:04 2019
Total time elapsed: 1.0m:31.97s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9499,
    "inflation_factor": 1.1474,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 9,
    "n_p_sig": 394,
    "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": 184848,
    "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": 1289166,
    "ldsc_nsnp_merge_regression_ld": 1289166,
    "ldsc_observed_scale_h2_beta": 0.0238,
    "ldsc_observed_scale_h2_se": 0.002,
    "ldsc_intercept_beta": 1.0336,
    "ldsc_intercept_se": 0.0072,
    "ldsc_lambda_gc": 1.1806,
    "ldsc_mean_chisq": 1.2046,
    "ldsc_ratio": 0.1642
}

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 1.000000 3 58 0 9837043 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 9851713 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 8.622834e+00 5.748291e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.000000 NA NA NA NA NA NA NA 7.886001e+07 5.628320e+07 828.0000000 3.259055e+07 6.948818e+07 1.145909e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA -5.380000e-05 1.141230e-02 -0.1756030 -3.556300e-03 1.300000e-06 3.547600e-03 1.967570e-01 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 8.112300e-03 7.685200e-03 0.0022672 2.777900e-03 4.658100e-03 1.074810e-02 1.185150e-01 ▇▁▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.806475e-01 2.939842e-01 0.0000000 2.200002e-01 4.700002e-01 7.400005e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.806486e-01 2.939596e-01 0.0000000 2.215315e-01 4.737716e-01 7.352044e-01 9.999998e-01 ▇▇▇▆▆
numeric AF 0 1.000000 NA NA NA NA NA NA NA 2.035095e-01 2.568664e-01 0.0009620 1.316800e-02 7.791700e-02 3.164500e-01 9.990370e-01 ▇▂▁▁▁
numeric AF_reference 184848 0.981237 NA NA NA NA NA NA NA 2.068410e-01 2.482930e-01 0.0000000 1.198080e-02 9.984030e-02 3.202880e-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 -0.0041886 0.0041739 0.3200000 0.3156103 0.623860 0.7821490 NA
1 54676 rs2462492 C T 0.0024294 0.0041341 0.5600000 0.5567679 0.400504 NA NA
1 86028 rs114608975 T C 0.0021572 0.0066139 0.7400005 0.7442972 0.103505 0.0277556 NA
1 91536 rs6702460 G T 0.0023972 0.0040688 0.5600000 0.5557520 0.456850 0.4207270 NA
1 234313 rs8179466 C T -0.0002667 0.0080342 0.9699999 0.9735190 0.074411 NA NA
1 534192 rs6680723 C T -0.0016792 0.0046500 0.7199992 0.7180049 0.241042 NA NA
1 546697 rs12025928 A G -0.0060374 0.0058059 0.2999998 0.2984029 0.913545 NA NA
1 693731 rs12238997 A G 0.0001352 0.0038961 0.9699999 0.9723096 0.116502 0.1417730 NA
1 705882 rs72631875 G A 0.0027228 0.0057156 0.6300007 0.6338022 0.067202 0.0315495 NA
1 706368 rs55727773 A G 0.0017437 0.0028876 0.5500004 0.5459483 0.515648 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A 0.0133692 0.0060598 0.0269998 0.0273692 0.041949 0.0473243 NA
22 51219766 rs182321900 C T 0.0021270 0.0284467 0.9400001 0.9403955 0.001918 NA NA
22 51220146 rs868950473 C T 0.0000201 0.0282213 1.0000000 0.9994315 0.001962 NA NA
22 51221190 rs369304721 G A 0.0107227 0.0060471 0.0759994 0.0761980 0.049751 NA NA
22 51221731 rs115055839 T C 0.0072445 0.0045226 0.1100001 0.1091860 0.073341 0.0625000 NA
22 51222100 rs114553188 G T -0.0069182 0.0053313 0.1900002 0.1944117 0.054400 0.0880591 NA
22 51223637 rs375798137 G A -0.0072280 0.0053566 0.1800002 0.1772231 0.054034 0.0788738 NA
22 51229805 rs9616985 T C 0.0072184 0.0045394 0.1100001 0.1117914 0.073172 0.0730831 NA
22 51232488 rs376461333 A G -0.0075130 0.0107608 0.4899999 0.4850655 0.019929 NA NA
22 51237063 rs3896457 T C 0.0058517 0.0027771 0.0350002 0.0351096 0.298237 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62386  ES:SE:LP:AF:ID  -0.00418859:0.0041739:0.49485:0.62386:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400504 ES:SE:LP:AF:ID  0.0024294:0.0041341:0.251812:0.400504:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103505 ES:SE:LP:AF:ID  0.00215724:0.00661389:0.130768:0.103505:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45685  ES:SE:LP:AF:ID  0.00239718:0.00406879:0.251812:0.45685:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074411 ES:SE:LP:AF:ID  -0.000266695:0.00803416:0.0132283:0.074411:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241042 ES:SE:LP:AF:ID  -0.00167922:0.00464995:0.142668:0.241042:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913545 ES:SE:LP:AF:ID  -0.00603737:0.00580592:0.522879:0.913545:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116502 ES:SE:LP:AF:ID  0.000135241:0.00389612:0.0132283:0.116502:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067202 ES:SE:LP:AF:ID  0.0027228:0.00571558:0.200659:0.067202:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515648 ES:SE:LP:AF:ID  0.00174368:0.00288764:0.259637:0.515648:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032823 ES:SE:LP:AF:ID  -0.000236101:0.00729976:0.0132283:0.032823:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036416 ES:SE:LP:AF:ID  0.000810864:0.00663071:0.0457575:0.036416:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036532 ES:SE:LP:AF:ID  0.000812871:0.00660512:0.0457575:0.036532:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036227 ES:SE:LP:AF:ID  0.000414171:0.00665397:0.0222764:0.036227:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016458 ES:SE:LP:AF:ID  -0.00426243:0.0102049:0.167491:0.016458:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03676  ES:SE:LP:AF:ID  0.000383373:0.00658019:0.0222764:0.03676:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036853 ES:SE:LP:AF:ID  0.000327604:0.00655785:0.0177288:0.036853:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101427 ES:SE:LP:AF:ID  0.00197174:0.00475659:0.167491:0.101427:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959372 ES:SE:LP:AF:ID  -0.00135314:0.00632829:0.0809219:0.959372:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031547 ES:SE:LP:AF:ID  0.00536559:0.0114009:0.19382:0.031547:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053174 ES:SE:LP:AF:ID  -0.0195264:0.00913099:1.49485:0.053174:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036383 ES:SE:LP:AF:ID  0.000121309:0.00659924:0.00436481:0.036383:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036701 ES:SE:LP:AF:ID  0.00095245:0.00653927:0.0555173:0.036701:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843311 ES:SE:LP:AF:ID  0.000806859:0.00337999:0.091515:0.843311:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05606  ES:SE:LP:AF:ID  -0.00104405:0.00546072:0.0705811:0.05606:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122471 ES:SE:LP:AF:ID  -0.00161944:0.00369653:0.180456:0.122471:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025805 ES:SE:LP:AF:ID  0.00300131:0.00907712:0.130768:0.025805:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121724 ES:SE:LP:AF:ID  -0.00125377:0.00369792:0.136677:0.121724:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132241 ES:SE:LP:AF:ID  -0.000928393:0.00364584:0.09691:0.132241:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011081 ES:SE:LP:AF:ID  0.00737807:0.0132776:0.236572:0.011081:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005696 ES:SE:LP:AF:ID  -0.0148995:0.017102:0.420216:0.005696:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002212 ES:SE:LP:AF:ID  -0.0188815:0.0291846:0.283997:0.002212:rs112573343
1   746189  rs139221807 A   G   .   PASS    AF=0.000996 ES:SE:LP:AF:ID  0.0930192:0.0481372:1.27572:0.000996:rs139221807
1   752478  rs146277091 G   A   .   PASS    AF=0.036592 ES:SE:LP:AF:ID  0.000402021:0.00647489:0.0222764:0.036592:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839062 ES:SE:LP:AF:ID  0.00135567:0.00327381:0.167491:0.839062:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838697 ES:SE:LP:AF:ID  0.00125173:0.00327042:0.154902:0.838697:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869695 ES:SE:LP:AF:ID  0.00111606:0.00350773:0.124939:0.869695:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129981 ES:SE:LP:AF:ID  -0.000552176:0.0035144:0.0555173:0.129981:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037082 ES:SE:LP:AF:ID  -0.000418474:0.00636634:0.0222764:0.037082:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037334 ES:SE:LP:AF:ID  -0.000154337:0.00632513:0.00877392:0.037334:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869045 ES:SE:LP:AF:ID  0.00067199:0.0035008:0.0705811:0.869045:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869146 ES:SE:LP:AF:ID  0.000629791:0.00350221:0.0655015:0.869146:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.03729  ES:SE:LP:AF:ID  2.74503e-05:0.00635248:-0:0.03729:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869046 ES:SE:LP:AF:ID  0.00070543:0.00350076:0.0757207:0.869046:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005124 ES:SE:LP:AF:ID  -0.0154129:0.0179691:0.408935:0.005124:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00509  ES:SE:LP:AF:ID  -0.0161581:0.0180166:0.431798:0.00509:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838143 ES:SE:LP:AF:ID  0.0012304:0.0032613:0.148742:0.838143:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037306 ES:SE:LP:AF:ID  0.000140858:0.00636145:0.00877392:0.037306:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838772 ES:SE:LP:AF:ID  0.0010595:0.00327057:0.124939:0.838772:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013777 ES:SE:LP:AF:ID  -0.0111855:0.0114067:0.481486:0.013777:rs181660517