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_5986.vcf.gz --id UKB-b:11076 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5986.txt.gz --cohort_controls 68426 --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-11076/UKB-b-11076_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11076/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-11076/UKB-b-11076_data.vcf.gz ...
Read summary statistics for 8696309 SNPs.
Dropped 7568 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, 1286068 SNPs remain.
After merging with regression SNP LD, 1286068 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0336 (0.0075)
Lambda GC: 1.0578
Mean Chi^2: 1.0571
Intercept: 1.0122 (0.0064)
Ratio: 0.2138 (0.1126)
Analysis finished at Thu Oct 17 14:41:55 2019
Total time elapsed: 1.0m:37.04s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9467,
    "inflation_factor": 1.0475,
    "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": 84418,
    "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": 1286068,
    "ldsc_nsnp_merge_regression_ld": 1286068,
    "ldsc_observed_scale_h2_beta": 0.0336,
    "ldsc_observed_scale_h2_se": 0.0075,
    "ldsc_intercept_beta": 1.0122,
    "ldsc_intercept_se": 0.0064,
    "ldsc_lambda_gc": 1.0578,
    "ldsc_mean_chisq": 1.0571,
    "ldsc_ratio": 0.2137
}

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 8688776 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 8696309 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 8.649415e+00 5.760862e+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 7.877090e+07 5.635988e+07 828.0000000 3.239313e+07 6.929985e+07 1.145659e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.330000e-05 1.552660e-02 -0.1833810 -6.457600e-03 -2.710000e-05 6.404000e-03 1.631170e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.263820e-02 8.732900e-03 0.0049390 5.832300e-03 8.657600e-03 1.714070e-02 8.892940e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.933870e-01 2.903422e-01 0.0000003 2.399999e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.933878e-01 2.903167e-01 0.0000003 2.400931e-01 4.907888e-01 7.449783e-01 9.999997e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.274263e-01 2.593105e-01 0.0051160 2.430900e-02 1.115450e-01 3.595750e-01 9.948840e-01 ▇▂▁▁▁
numeric AF_reference 84418 0.9902927 NA NA NA NA NA NA NA 2.272634e-01 2.512995e-01 0.0000000 2.216450e-02 1.281950e-01 3.568290e-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 -0.0009392 0.0091245 0.9199999 0.9180131 0.623987 0.7821490 NA
1 54676 rs2462492 C T -0.0066609 0.0090683 0.4600002 0.4626290 0.399180 NA NA
1 86028 rs114608975 T C -0.0140908 0.0143735 0.3300000 0.3269223 0.104002 0.0277556 NA
1 91536 rs6702460 G T -0.0171649 0.0089216 0.0539995 0.0543571 0.456263 0.4207270 NA
1 234313 rs8179466 C T -0.0143629 0.0174390 0.4100001 0.4101623 0.074922 NA NA
1 534192 rs6680723 C T 0.0054805 0.0101974 0.5900000 0.5909651 0.240514 NA NA
1 546697 rs12025928 A G -0.0105570 0.0126554 0.4000000 0.4041743 0.912581 NA NA
1 693731 rs12238997 A G 0.0033972 0.0084914 0.6899999 0.6891019 0.117125 0.1417730 NA
1 705882 rs72631875 G A 0.0008319 0.0123740 0.9500000 0.9463984 0.067985 0.0315495 NA
1 706368 rs55727773 A G -0.0079422 0.0062882 0.2099999 0.2065742 0.513999 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0029182 0.0076033 0.6999999 0.7011264 0.136759 0.2052720 NA
22 51219387 rs9616832 T C 0.0016363 0.0099244 0.8700001 0.8690437 0.072276 0.0654952 NA
22 51219704 rs147475742 G A -0.0013791 0.0132781 0.9199999 0.9172807 0.041093 0.0473243 NA
22 51221190 rs369304721 G A 0.0011538 0.0133296 0.9299999 0.9310219 0.048575 NA NA
22 51221731 rs115055839 T C 0.0023158 0.0099267 0.8200001 0.8155401 0.071785 0.0625000 NA
22 51222100 rs114553188 G T -0.0028152 0.0115879 0.8100000 0.8080488 0.054300 0.0880591 NA
22 51223637 rs375798137 G A -0.0033062 0.0116483 0.7800007 0.7765385 0.053911 0.0788738 NA
22 51229805 rs9616985 T C 0.0017075 0.0099626 0.8600001 0.8639192 0.071663 0.0730831 NA
22 51232488 rs376461333 A G -0.0047079 0.0235434 0.8400000 0.8415052 0.019937 NA NA
22 51237063 rs3896457 T C -0.0044960 0.0060459 0.4600002 0.4570961 0.297836 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623987 ES:SE:LP:AF:ID  -0.000939248:0.00912451:0.0362122:0.623987:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.39918  ES:SE:LP:AF:ID  -0.00666092:0.00906834:0.337242:0.39918:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104002 ES:SE:LP:AF:ID  -0.0140908:0.0143735:0.481486:0.104002:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456263 ES:SE:LP:AF:ID  -0.0171649:0.00892156:1.26761:0.456263:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074922 ES:SE:LP:AF:ID  -0.0143629:0.017439:0.387216:0.074922:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240514 ES:SE:LP:AF:ID  0.00548047:0.0101974:0.229148:0.240514:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912581 ES:SE:LP:AF:ID  -0.010557:0.0126554:0.39794:0.912581:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117125 ES:SE:LP:AF:ID  0.00339717:0.00849135:0.161151:0.117125:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067985 ES:SE:LP:AF:ID  0.000831907:0.012374:0.0222764:0.067985:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513999 ES:SE:LP:AF:ID  -0.00794222:0.00628817:0.677781:0.513999:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033768 ES:SE:LP:AF:ID  0.000178096:0.0156425:0.00436481:0.033768:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037466 ES:SE:LP:AF:ID  0.0039907:0.0142233:0.107905:0.037466:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037558 ES:SE:LP:AF:ID  0.00487055:0.0141739:0.136677:0.037558:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037242 ES:SE:LP:AF:ID  0.00579373:0.0142759:0.167491:0.037242:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016297 ES:SE:LP:AF:ID  0.0252481:0.0223824:0.585027:0.016297:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037839 ES:SE:LP:AF:ID  0.00499417:0.0141115:0.142668:0.037839:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03792  ES:SE:LP:AF:ID  0.00330279:0.0140677:0.091515:0.03792:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101533 ES:SE:LP:AF:ID  -0.00586758:0.010387:0.244125:0.101533:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.957853 ES:SE:LP:AF:ID  -0.00938563:0.0135374:0.309804:0.957853:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031543 ES:SE:LP:AF:ID  0.0522067:0.0251838:1.42022:0.031543:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052627 ES:SE:LP:AF:ID  -0.00341471:0.0199887:0.0655015:0.052627:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037373 ES:SE:LP:AF:ID  0.00505876:0.0141668:0.142668:0.037373:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037703 ES:SE:LP:AF:ID  0.00512604:0.0140442:0.142668:0.037703:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841145 ES:SE:LP:AF:ID  0.000135136:0.00733876:0.00436481:0.841145:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05603  ES:SE:LP:AF:ID  -0.0198818:0.0119413:1.01773:0.05603:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123133 ES:SE:LP:AF:ID  -0.00193238:0.00805413:0.091515:0.123133:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025594 ES:SE:LP:AF:ID  -0.0300648:0.0199144:0.886057:0.025594:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122315 ES:SE:LP:AF:ID  -0.00247024:0.00805999:0.119186:0.122315:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133576 ES:SE:LP:AF:ID  -0.00275759:0.00792729:0.136677:0.133576:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011128 ES:SE:LP:AF:ID  -0.00658989:0.0289458:0.0861861:0.011128:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006311 ES:SE:LP:AF:ID  0.0378189:0.0353019:0.552842:0.006311:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037638 ES:SE:LP:AF:ID  0.00246315:0.0138997:0.0655015:0.037638:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837051 ES:SE:LP:AF:ID  -0.00105546:0.0071023:0.0555173:0.837051:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836575 ES:SE:LP:AF:ID  -0.000820415:0.00709322:0.0409586:0.836575:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868582 ES:SE:LP:AF:ID  -7.45142e-05:0.00762033:0.00436481:0.868582:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131122 ES:SE:LP:AF:ID  -0.000820972:0.00763557:0.0409586:0.131122:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038062 ES:SE:LP:AF:ID  0.00199838:0.013691:0.0555173:0.038062:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038304 ES:SE:LP:AF:ID  0.00188788:0.0136079:0.05061:0.038304:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867862 ES:SE:LP:AF:ID  -7.97652e-05:0.00760503:0.00436481:0.867862:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867967 ES:SE:LP:AF:ID  6.72976e-05:0.00760893:0.00436481:0.867967:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038284 ES:SE:LP:AF:ID  0.00191684:0.0136576:0.05061:0.038284:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867883 ES:SE:LP:AF:ID  0.000195658:0.00760485:0.00877392:0.867883:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005276 ES:SE:LP:AF:ID  -0.0415004:0.0385262:0.552842:0.005276:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005249 ES:SE:LP:AF:ID  -0.0418603:0.0386201:0.552842:0.005249:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.836137 ES:SE:LP:AF:ID  -0.00102603:0.00708022:0.0555173:0.836137:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038303 ES:SE:LP:AF:ID  0.00232358:0.0136763:0.0604807:0.038303:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836768 ES:SE:LP:AF:ID  -0.00116175:0.00709974:0.0604807:0.836768:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.012842 ES:SE:LP:AF:ID  -0.0401871:0.0257973:0.920819:0.012842:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005538 ES:SE:LP:AF:ID  -0.0100115:0.0384225:0.102373:0.005538:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838175 ES:SE:LP:AF:ID  -0.00176214:0.00720089:0.091515:0.838175:rs3131965