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_2867.vcf.gz --id UKB-b:2047 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2867.txt.gz --cohort_controls 114449 --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-2047/UKB-b-2047_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2047/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2047/UKB-b-2047_data.vcf.gz ...
Read summary statistics for 9119001 SNPs.
Dropped 9269 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, 1287472 SNPs remain.
After merging with regression SNP LD, 1287472 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0705 (0.0056)
Lambda GC: 1.1725
Mean Chi^2: 1.1877
Intercept: 1.031 (0.0072)
Ratio: 0.165 (0.0381)
Analysis finished at Thu Oct 17 14:43:44 2019
Total time elapsed: 1.0m:34.42s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.948,
    "inflation_factor": 1.1474,
    "mean_EFFECT": -8.7111e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 9,
    "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": 99442,
    "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": 1287472,
    "ldsc_nsnp_merge_regression_ld": 1287472,
    "ldsc_observed_scale_h2_beta": 0.0705,
    "ldsc_observed_scale_h2_se": 0.0056,
    "ldsc_intercept_beta": 1.031,
    "ldsc_intercept_se": 0.0072,
    "ldsc_lambda_gc": 1.1725,
    "ldsc_mean_chisq": 1.1877,
    "ldsc_ratio": 0.1652
}

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 9109775 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 9119001 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 8.639881e+00 5.756646e+00 1.000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.879577e+07 5.632514e+07 828.000000 3.246024e+07 6.936938e+07 1.145265e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -8.700000e-06 1.439360e-02 -0.163614 -5.642600e-03 1.850000e-05 5.659600e-03 1.605210e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.117020e-02 8.407600e-03 0.003939 4.715400e-03 7.304700e-03 1.532860e-02 9.250530e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.804658e-01 2.940866e-01 0.000000 2.200002e-01 4.700002e-01 7.400005e-01 1.000000e+00 ▇▆▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.804682e-01 2.940602e-01 0.000000 2.210981e-01 4.736201e-01 7.351457e-01 9.999998e-01 ▇▇▇▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.177029e-01 2.582625e-01 0.003059 1.928800e-02 9.800900e-02 3.428880e-01 9.969410e-01 ▇▂▁▁▁
numeric AF_reference 99442 0.9890951 NA NA NA NA NA NA NA 2.182126e-01 2.501289e-01 0.000000 1.637380e-02 1.158150e-01 3.416530e-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 0.0007699 0.0072680 0.9199999 0.9156364 0.624916 0.7821490 NA
1 54676 rs2462492 C T 0.0061751 0.0071804 0.3900004 0.3897932 0.400620 NA NA
1 86028 rs114608975 T C 0.0119835 0.0114631 0.2999998 0.2958391 0.103686 0.0277556 NA
1 91536 rs6702460 G T 0.0080244 0.0070651 0.2599998 0.2560523 0.457691 0.4207270 NA
1 234313 rs8179466 C T 0.0071916 0.0138779 0.5999997 0.6043166 0.074833 NA NA
1 534192 rs6680723 C T 0.0036517 0.0081135 0.6499995 0.6526561 0.240575 NA NA
1 546697 rs12025928 A G 0.0026733 0.0100778 0.7899998 0.7908051 0.913458 NA NA
1 693731 rs12238997 A G 0.0127619 0.0067973 0.0599998 0.0604510 0.115626 0.1417730 NA
1 705882 rs72631875 G A 0.0034983 0.0099498 0.7300002 0.7251417 0.066991 0.0315495 NA
1 706368 rs55727773 A G 0.0015560 0.0050265 0.7600007 0.7568975 0.515530 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0016082 0.0060616 0.7899998 0.7907675 0.138281 0.2052720 NA
22 51219387 rs9616832 T C -0.0030944 0.0078583 0.6899999 0.6937507 0.073838 0.0654952 NA
22 51219704 rs147475742 G A -0.0002928 0.0105326 0.9800000 0.9778186 0.041979 0.0473243 NA
22 51221190 rs369304721 G A -0.0056233 0.0105378 0.5900000 0.5935940 0.049626 NA NA
22 51221731 rs115055839 T C -0.0036051 0.0078637 0.6499995 0.6466297 0.073304 0.0625000 NA
22 51222100 rs114553188 G T -0.0056290 0.0092472 0.5400003 0.5427054 0.054578 0.0880591 NA
22 51223637 rs375798137 G A -0.0059736 0.0092918 0.5199996 0.5202977 0.054202 0.0788738 NA
22 51229805 rs9616985 T C -0.0028364 0.0078913 0.7199992 0.7192665 0.073168 0.0730831 NA
22 51232488 rs376461333 A G -0.0141554 0.0186540 0.4500005 0.4479483 0.020054 NA NA
22 51237063 rs3896457 T C -0.0044458 0.0048249 0.3599996 0.3568312 0.298915 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.624916 ES:SE:LP:AF:ID  0.000769915:0.00726802:0.0362122:0.624916:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.40062  ES:SE:LP:AF:ID  0.00617509:0.0071804:0.408935:0.40062:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103686 ES:SE:LP:AF:ID  0.0119835:0.0114631:0.522879:0.103686:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457691 ES:SE:LP:AF:ID  0.00802436:0.00706512:0.585027:0.457691:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074833 ES:SE:LP:AF:ID  0.00719157:0.0138779:0.221849:0.074833:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240575 ES:SE:LP:AF:ID  0.0036517:0.00811353:0.187087:0.240575:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913458 ES:SE:LP:AF:ID  0.00267329:0.0100778:0.102373:0.913458:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.115626 ES:SE:LP:AF:ID  0.0127619:0.00679732:1.22185:0.115626:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.066991 ES:SE:LP:AF:ID  0.0034983:0.00994979:0.136677:0.066991:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.51553  ES:SE:LP:AF:ID  0.001556:0.00502653:0.119186:0.51553:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032958 ES:SE:LP:AF:ID  -0.0090995:0.0126575:0.327902:0.032958:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036585 ES:SE:LP:AF:ID  -0.00668502:0.0114971:0.251812:0.036585:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036716 ES:SE:LP:AF:ID  -0.00705153:0.0114499:0.267606:0.036716:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.03639  ES:SE:LP:AF:ID  -0.00804283:0.0115367:0.309804:0.03639:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016482 ES:SE:LP:AF:ID  0.038487:0.017628:1.5376:0.016482:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036944 ES:SE:LP:AF:ID  -0.00791854:0.0114041:0.309804:0.036944:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037042 ES:SE:LP:AF:ID  -0.00780755:0.0113666:0.309804:0.037042:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101253 ES:SE:LP:AF:ID  -0.0114158:0.0082762:0.769551:0.101253:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959047 ES:SE:LP:AF:ID  0.0100026:0.0109611:0.443698:0.959047:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031576 ES:SE:LP:AF:ID  0.0145993:0.0197546:0.337242:0.031576:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053203 ES:SE:LP:AF:ID  -0.0266395:0.0158684:1.03152:0.053203:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036568 ES:SE:LP:AF:ID  -0.00822204:0.0114408:0.327902:0.036568:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03681  ES:SE:LP:AF:ID  -0.00717069:0.0113488:0.275724:0.03681:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843706 ES:SE:LP:AF:ID  -0.00576926:0.00587696:0.481486:0.843706:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055268 ES:SE:LP:AF:ID  0.00372422:0.00957582:0.154902:0.055268:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121898 ES:SE:LP:AF:ID  0.0100339:0.00643187:0.920819:0.121898:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025756 ES:SE:LP:AF:ID  -0.0191608:0.0157949:0.638272:0.025756:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121156 ES:SE:LP:AF:ID  0.010701:0.00643371:1.01773:0.121156:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.131818 ES:SE:LP:AF:ID  0.00315652:0.00635332:0.207608:0.131818:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.010965 ES:SE:LP:AF:ID  0.00678028:0.0232573:0.113509:0.010965:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005859 ES:SE:LP:AF:ID  -0.00433679:0.0294266:0.0555173:0.005859:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036795 ES:SE:LP:AF:ID  -0.00899275:0.0112203:0.376751:0.036795:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839443 ES:SE:LP:AF:ID  -0.00816226:0.00569529:0.823909:0.839443:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83909  ES:SE:LP:AF:ID  -0.00778249:0.00568918:0.769551:0.83909:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.870062 ES:SE:LP:AF:ID  -0.0118807:0.00609916:1.29243:0.870062:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129582 ES:SE:LP:AF:ID  0.011306:0.00611171:1.19382:0.129582:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03727  ES:SE:LP:AF:ID  -0.00914101:0.0110354:0.387216:0.03727:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037528 ES:SE:LP:AF:ID  -0.00862981:0.0109621:0.366532:0.037528:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869393 ES:SE:LP:AF:ID  -0.0114537:0.00608703:1.22185:0.869393:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869497 ES:SE:LP:AF:ID  -0.0115818:0.00608924:1.24413:0.869497:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037479 ES:SE:LP:AF:ID  -0.00982002:0.0110117:0.431798:0.037479:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869402 ES:SE:LP:AF:ID  -0.0115163:0.00608728:1.22915:0.869402:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005037 ES:SE:LP:AF:ID  0.0151457:0.0314875:0.200659:0.005037:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005005 ES:SE:LP:AF:ID  0.0158261:0.031552:0.207608:0.005005:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838526 ES:SE:LP:AF:ID  -0.00785477:0.00567353:0.769551:0.838526:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037488 ES:SE:LP:AF:ID  -0.0098568:0.0110287:0.431798:0.037488:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.839131 ES:SE:LP:AF:ID  -0.0078568:0.00568907:0.769551:0.839131:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013859 ES:SE:LP:AF:ID  0.0336674:0.0197354:1.05552:0.013859:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.00551  ES:SE:LP:AF:ID  -0.0720253:0.0307195:1.72125:0.00551:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.840194 ES:SE:LP:AF:ID  -0.00904054:0.00576431:0.920819:0.840194:rs3131965