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_101970.vcf.gz --id UKB-b:1255 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_101970.txt.gz --cohort_controls 64941 --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-1255/UKB-b-1255_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1255/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:05 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-1255/UKB-b-1255_data.vcf.gz ...
Read summary statistics for 8624985 SNPs.
Dropped 7371 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, 1285751 SNPs remain.
After merging with regression SNP LD, 1285751 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0046 (0.0065)
Lambda GC: 1.004
Mean Chi^2: 1.0021
Intercept: 0.9963 (0.0056)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:43:41 2019
Total time elapsed: 1.0m:35.92s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": -0,
    "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": 83094,
    "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": 1285751,
    "ldsc_nsnp_merge_regression_ld": 1285751,
    "ldsc_observed_scale_h2_beta": 0.0046,
    "ldsc_observed_scale_h2_se": 0.0065,
    "ldsc_intercept_beta": 0.9963,
    "ldsc_intercept_se": 0.0056,
    "ldsc_lambda_gc": 1.004,
    "ldsc_mean_chisq": 1.0021,
    "ldsc_ratio": -1.7619
}

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 8617648 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 8624985 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 8.650329e+00 5.760982e+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 7.877011e+07 5.637171e+07 828.0000000 3.238533e+07 6.929181e+07 1.145688e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -2.760000e-05 7.030300e-03 -0.0681829 -2.967400e-03 -5.600000e-05 2.846300e-03 8.589480e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.796500e-03 3.956700e-03 0.0023021 2.711000e-03 3.997200e-03 7.835600e-03 3.968980e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.994214e-01 2.888329e-01 0.0000002 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 4.994202e-01 2.888068e-01 0.0000002 2.494128e-01 4.990579e-01 7.497896e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291177e-01 2.594886e-01 0.0053900 2.519000e-02 1.138970e-01 3.626200e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83094 0.9903659 NA NA NA NA NA NA NA 2.288669e-01 2.514593e-01 0.0000000 2.316290e-02 1.303910e-01 3.596250e-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 0.0038991 0.0042427 0.3599996 0.3580898 0.623797 0.7821490 NA
1 54676 rs2462492 C T -0.0049200 0.0042308 0.2399999 0.2448735 0.399125 NA NA
1 86028 rs114608975 T C -0.0052213 0.0067338 0.4400003 0.4381145 0.103538 0.0277556 NA
1 91536 rs6702460 G T 0.0007483 0.0041611 0.8600001 0.8572843 0.455904 0.4207270 NA
1 234313 rs8179466 C T -0.0021260 0.0082285 0.8000000 0.7961220 0.074461 NA NA
1 534192 rs6680723 C T 0.0001698 0.0047394 0.9699999 0.9714209 0.242052 NA NA
1 546697 rs12025928 A G 0.0049249 0.0058803 0.4000000 0.4022993 0.912863 NA NA
1 693731 rs12238997 A G -0.0069181 0.0039517 0.0800000 0.0800031 0.117305 0.1417730 NA
1 705882 rs72631875 G A 0.0015860 0.0057604 0.7800007 0.7830614 0.067689 0.0315495 NA
1 706368 rs55727773 A G 0.0004614 0.0029333 0.8800001 0.8750232 0.513311 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0017495 0.0035676 0.6200004 0.6238650 0.136300 0.2052720 NA
22 51219387 rs9616832 T C -0.0016663 0.0046500 0.7199992 0.7200879 0.071791 0.0654952 NA
22 51219704 rs147475742 G A -0.0025489 0.0061855 0.6800001 0.6802888 0.041189 0.0473243 NA
22 51221190 rs369304721 G A -0.0007446 0.0062296 0.9000000 0.9048597 0.048367 NA NA
22 51221731 rs115055839 T C -0.0012892 0.0046510 0.7800007 0.7816277 0.071341 0.0625000 NA
22 51222100 rs114553188 G T 0.0024755 0.0053897 0.6499995 0.6460120 0.054842 0.0880591 NA
22 51223637 rs375798137 G A 0.0024341 0.0054178 0.6499995 0.6532373 0.054462 0.0788738 NA
22 51229805 rs9616985 T C -0.0013252 0.0046654 0.7800007 0.7763664 0.071246 0.0730831 NA
22 51232488 rs376461333 A G 0.0086713 0.0107476 0.4199997 0.4197749 0.020452 NA NA
22 51237063 rs3896457 T C 0.0039420 0.0028163 0.1600000 0.1616050 0.298394 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623797 ES:SE:LP:AF:ID  0.00389907:0.00424268:0.443698:0.623797:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399125 ES:SE:LP:AF:ID  -0.00491995:0.00423079:0.619789:0.399125:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103538 ES:SE:LP:AF:ID  -0.0052213:0.00673385:0.356547:0.103538:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455904 ES:SE:LP:AF:ID  0.000748296:0.00416108:0.0655015:0.455904:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074461 ES:SE:LP:AF:ID  -0.00212598:0.00822846:0.09691:0.074461:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242052 ES:SE:LP:AF:ID  0.000169794:0.00473938:0.0132283:0.242052:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912863 ES:SE:LP:AF:ID  0.00492488:0.0058803:0.39794:0.912863:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117305 ES:SE:LP:AF:ID  -0.00691808:0.00395168:1.09691:0.117305:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067689 ES:SE:LP:AF:ID  0.00158601:0.00576036:0.107905:0.067689:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513311 ES:SE:LP:AF:ID  0.000461353:0.0029333:0.0555173:0.513311:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033681 ES:SE:LP:AF:ID  0.00330962:0.00731063:0.187087:0.033681:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037462 ES:SE:LP:AF:ID  0.000294177:0.00663038:0.0177288:0.037462:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037649 ES:SE:LP:AF:ID  -6.05807e-05:0.00659569:0.00436481:0.037649:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037225 ES:SE:LP:AF:ID  -0.00140418:0.00665596:0.0809219:0.037225:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016284 ES:SE:LP:AF:ID  -0.0182002:0.0104403:1.09151:0.016284:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037865 ES:SE:LP:AF:ID  0.000147863:0.00657314:0.00877392:0.037865:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037959 ES:SE:LP:AF:ID  0.000804055:0.00655229:0.0457575:0.037959:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102737 ES:SE:LP:AF:ID  0.00602985:0.00478649:0.677781:0.102737:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958085 ES:SE:LP:AF:ID  0.000815131:0.00632853:0.0457575:0.958085:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031694 ES:SE:LP:AF:ID  0.00341151:0.0115886:0.113509:0.031694:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052723 ES:SE:LP:AF:ID  -0.00341272:0.00933443:0.148742:0.052723:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037453 ES:SE:LP:AF:ID  0.000720056:0.00659531:0.0409586:0.037453:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037722 ES:SE:LP:AF:ID  0.000875849:0.00654068:0.05061:0.037722:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841444 ES:SE:LP:AF:ID  0.00497489:0.0034174:0.823909:0.841444:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056319 ES:SE:LP:AF:ID  -0.0120114:0.00555203:1.50864:0.056319:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12307  ES:SE:LP:AF:ID  -0.00653268:0.00375363:1.08619:0.12307:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025133 ES:SE:LP:AF:ID  0.01216:0.00934714:0.721246:0.025133:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122322 ES:SE:LP:AF:ID  -0.00632455:0.00375484:1.03621:0.122322:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134133 ES:SE:LP:AF:ID  -0.00409555:0.00368597:0.568636:0.134133:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011559 ES:SE:LP:AF:ID  -0.00848472:0.013136:0.283997:0.011559:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  -0.0167022:0.0166811:0.49485:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037604 ES:SE:LP:AF:ID  0.00112174:0.00648007:0.0655015:0.037604:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837032 ES:SE:LP:AF:ID  0.00436541:0.00330592:0.721246:0.837032:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836736 ES:SE:LP:AF:ID  0.00446645:0.00330349:0.744727:0.836736:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868569 ES:SE:LP:AF:ID  0.00582561:0.00354981:1:0.868569:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130997 ES:SE:LP:AF:ID  -0.00612369:0.00355893:1.07058:0.130997:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03805  ES:SE:LP:AF:ID  0.000606301:0.00637842:0.0362122:0.03805:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038296 ES:SE:LP:AF:ID  0.000471419:0.00633889:0.0268721:0.038296:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867983 ES:SE:LP:AF:ID  0.00595572:0.0035441:1.03152:0.867983:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868057 ES:SE:LP:AF:ID  0.00602397:0.00354555:1.05061:0.868057:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038204 ES:SE:LP:AF:ID  0.000948296:0.00636816:0.0555173:0.038204:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867994 ES:SE:LP:AF:ID  0.00594548:0.00354402:1.03152:0.867994:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  -0.0027003:0.0177613:0.0555173:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836162 ES:SE:LP:AF:ID  0.00459268:0.00329382:0.79588:0.836162:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038207 ES:SE:LP:AF:ID  0.00133277:0.00637746:0.0809219:0.038207:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836796 ES:SE:LP:AF:ID  0.00464505:0.00330285:0.79588:0.836796:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01303  ES:SE:LP:AF:ID  -0.0265617:0.0119487:1.58503:0.01303:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  0.0147066:0.017738:0.387216:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838113 ES:SE:LP:AF:ID  0.00400172:0.00334935:0.638272:0.838113:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868235 ES:SE:LP:AF:ID  0.0058118:0.00353945:1:0.868235:rs3115858