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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}

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-151/UKB-b-151_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-151/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:59 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-151/UKB-b-151_data.vcf.gz ...
Read summary statistics for 9851866 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: 0.0358 (0.0019)
Lambda GC: 1.2811
Mean Chi^2: 1.3386
Intercept: 1.0306 (0.0081)
Ratio: 0.0904 (0.0238)
Analysis finished at Thu Oct 17 14:45:58 2019
Total time elapsed: 1.0m:58.48s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9499,
    "inflation_factor": 1.1999,
    "mean_EFFECT": 0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 11,
    "n_p_sig": 1727,
    "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": 184849,
    "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": 0.0358,
    "ldsc_observed_scale_h2_se": 0.0019,
    "ldsc_intercept_beta": 1.0306,
    "ldsc_intercept_se": 0.0081,
    "ldsc_lambda_gc": 1.2811,
    "ldsc_mean_chisq": 1.3386,
    "ldsc_ratio": 0.0904
}

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 TRUE
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 9837196 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 9851866 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 8.622825e+00 5.748290e+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 7.886027e+07 5.628334e+07 828.0000000 3.259061e+07 6.948835e+07 1.145912e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 1.339000e-04 2.023920e-02 -0.3063710 -6.412100e-03 4.370000e-05 6.540500e-03 2.942990e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.426210e-02 1.350190e-02 0.0039897 4.885100e-03 8.191700e-03 1.890160e-02 2.095390e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.711390e-01 2.968846e-01 0.0000000 2.099999e-01 4.600002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.711414e-01 2.968604e-01 0.0000000 2.067443e-01 4.609641e-01 7.289058e-01 9.999996e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.035069e-01 2.568629e-01 0.0009770 1.316800e-02 7.791000e-02 3.164638e-01 9.990200e-01 ▇▂▁▁▁
numeric AF_reference 184849 0.9812372 NA NA NA NA NA NA NA 2.068392e-01 2.482924e-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 -0.0083486 0.0073417 0.2599998 0.2554760 0.623794 0.7821490 NA
1 54676 rs2462492 C T 0.0071344 0.0072739 0.3300000 0.3266799 0.400372 NA NA
1 86028 rs114608975 T C 0.0148180 0.0116306 0.2000000 0.2026447 0.103537 0.0277556 NA
1 91536 rs6702460 G T 0.0061392 0.0071632 0.3900004 0.3914131 0.456879 0.4207270 NA
1 234313 rs8179466 C T -0.0035413 0.0141357 0.8000000 0.8021828 0.074421 NA NA
1 534192 rs6680723 C T 0.0031211 0.0081853 0.6999999 0.7029757 0.240935 NA NA
1 546697 rs12025928 A G -0.0042252 0.0102001 0.6800001 0.6787048 0.913419 NA NA
1 693731 rs12238997 A G -0.0022462 0.0068542 0.7400005 0.7431309 0.116396 0.1417730 NA
1 705882 rs72631875 G A 0.0079732 0.0100427 0.4299995 0.4272391 0.067316 0.0315495 NA
1 706368 rs55727773 A G -0.0039647 0.0050765 0.4299995 0.4348127 0.515589 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0181343 0.0106624 0.0890000 0.0889860 0.041952 0.0473243 NA
22 51219766 rs182321900 C T 0.0089009 0.0496397 0.8600001 0.8576937 0.001938 NA NA
22 51220146 rs868950473 C T -0.0005076 0.0491486 0.9900000 0.9917596 0.001989 NA NA
22 51221190 rs369304721 G A -0.0209498 0.0106426 0.0490004 0.0490122 0.049742 NA NA
22 51221731 rs115055839 T C -0.0217075 0.0079604 0.0064000 0.0063928 0.073231 0.0625000 NA
22 51222100 rs114553188 G T -0.0003227 0.0093730 0.9699999 0.9725345 0.054417 0.0880591 NA
22 51223637 rs375798137 G A -0.0005364 0.0094185 0.9500000 0.9545800 0.054045 0.0788738 NA
22 51229805 rs9616985 T C -0.0211045 0.0079886 0.0081999 0.0082457 0.073074 0.0730831 NA
22 51232488 rs376461333 A G 0.0009069 0.0188376 0.9599999 0.9616016 0.020001 NA NA
22 51237063 rs3896457 T C 0.0003212 0.0048865 0.9500000 0.9475896 0.298114 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623794 ES:SE:LP:AF:ID  -0.00834863:0.00734172:0.585027:0.623794:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400372 ES:SE:LP:AF:ID  0.00713445:0.00727394:0.481486:0.400372:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103537 ES:SE:LP:AF:ID  0.014818:0.0116306:0.69897:0.103537:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456879 ES:SE:LP:AF:ID  0.00613924:0.00716316:0.408935:0.456879:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074421 ES:SE:LP:AF:ID  -0.00354132:0.0141357:0.09691:0.074421:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240935 ES:SE:LP:AF:ID  0.00312111:0.0081853:0.154902:0.240935:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913419 ES:SE:LP:AF:ID  -0.0042252:0.0102001:0.167491:0.913419:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116396 ES:SE:LP:AF:ID  -0.00224618:0.00685417:0.130768:0.116396:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067316 ES:SE:LP:AF:ID  0.00797315:0.0100427:0.366532:0.067316:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515589 ES:SE:LP:AF:ID  -0.00396469:0.00507654:0.366532:0.515589:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.03297  ES:SE:LP:AF:ID  -0.0241688:0.012809:1.22915:0.03297:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036595 ES:SE:LP:AF:ID  -0.0234904:0.0116326:1.36653:0.036595:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036712 ES:SE:LP:AF:ID  -0.0225331:0.0115886:1.284:0.036712:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036415 ES:SE:LP:AF:ID  -0.0232406:0.0116713:1.33724:0.036415:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016462 ES:SE:LP:AF:ID  -0.0352391:0.0179409:1.30103:0.016462:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036954 ES:SE:LP:AF:ID  -0.0212398:0.0115425:1.18046:0.036954:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037048 ES:SE:LP:AF:ID  -0.0204564:0.0115032:1.12494:0.037048:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101305 ES:SE:LP:AF:ID  0.00149206:0.0083725:0.0655015:0.101305:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959115 ES:SE:LP:AF:ID  0.0165402:0.0110941:0.853872:0.959115:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031401 ES:SE:LP:AF:ID  -0.000141112:0.0201652:0.00436481:0.031401:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053277 ES:SE:LP:AF:ID  -0.0236015:0.0160143:0.853872:0.053277:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036561 ES:SE:LP:AF:ID  -0.0189023:0.0115789:1:0.036561:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036875 ES:SE:LP:AF:ID  -0.020844:0.0114731:1.16115:0.036875:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843147 ES:SE:LP:AF:ID  0.00663858:0.00594132:0.585027:0.843147:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055934 ES:SE:LP:AF:ID  -0.00231392:0.00961646:0.091515:0.055934:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122364 ES:SE:LP:AF:ID  -0.0012808:0.00650292:0.0757207:0.122364:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025649 ES:SE:LP:AF:ID  0.00408561:0.0160161:0.09691:0.025649:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121603 ES:SE:LP:AF:ID  -0.00119219:0.00650555:0.0705811:0.121603:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132322 ES:SE:LP:AF:ID  -0.00243481:0.00641175:0.154902:0.132322:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.01112  ES:SE:LP:AF:ID  0.00218417:0.0233348:0.0315171:0.01112:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005711 ES:SE:LP:AF:ID  0.0237456:0.0300581:0.366532:0.005711:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002264 ES:SE:LP:AF:ID  0.0427138:0.0506609:0.39794:0.002264:rs112573343
1   746189  rs139221807 A   G   .   PASS    AF=0.001033 ES:SE:LP:AF:ID  0.0184043:0.0824419:0.0861861:0.001033:rs139221807
1   752478  rs146277091 G   A   .   PASS    AF=0.036783 ES:SE:LP:AF:ID  -0.0212552:0.0113589:1.21467:0.036783:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.83892  ES:SE:LP:AF:ID  0.00434918:0.0057537:0.346787:0.83892:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838541 ES:SE:LP:AF:ID  0.00448639:0.00574737:0.356547:0.838541:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869739 ES:SE:LP:AF:ID  -0.00143543:0.00616697:0.0861861:0.869739:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129919 ES:SE:LP:AF:ID  0.000962013:0.0061794:0.0555173:0.129919:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03729  ES:SE:LP:AF:ID  -0.0184779:0.011166:1.00877:0.03729:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037531 ES:SE:LP:AF:ID  -0.0179274:0.0110957:0.958607:0.037531:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869071 ES:SE:LP:AF:ID  -0.00134453:0.00615473:0.0809219:0.869071:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869172 ES:SE:LP:AF:ID  -0.001131:0.00615726:0.0705811:0.869172:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.03749  ES:SE:LP:AF:ID  -0.0176144:0.0111434:0.958607:0.03749:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869074 ES:SE:LP:AF:ID  -0.00146886:0.00615462:0.091515:0.869074:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005118 ES:SE:LP:AF:ID  0.0329283:0.0316147:0.522879:0.005118:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005084 ES:SE:LP:AF:ID  0.0359689:0.0316985:0.585027:0.005084:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837998 ES:SE:LP:AF:ID  0.00389115:0.0057316:0.30103:0.837998:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037503 ES:SE:LP:AF:ID  -0.0167848:0.0111589:0.886057:0.037503:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838631 ES:SE:LP:AF:ID  0.00358252:0.00574774:0.275724:0.838631:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013747 ES:SE:LP:AF:ID  -0.0338274:0.0200789:1.03621:0.013747:rs181660517