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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}

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-5593/UKB-b-5593_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5593/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:28 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-5593/UKB-b-5593_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: 0.0164 (0.0016)
Lambda GC: 1.3104
Mean Chi^2: 1.3257
Intercept: 1.1791 (0.0075)
Ratio: 0.5499 (0.023)
Analysis finished at Thu Oct 17 14:47:01 2019
Total time elapsed: 1.0m:33.2s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9499,
    "inflation_factor": 1.2544,
    "mean_EFFECT": 0.0002,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 7,
    "n_p_sig": 82,
    "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": 0.0164,
    "ldsc_observed_scale_h2_se": 0.0016,
    "ldsc_intercept_beta": 1.1791,
    "ldsc_intercept_se": 0.0075,
    "ldsc_lambda_gc": 1.3104,
    "ldsc_mean_chisq": 1.3257,
    "ldsc_ratio": 0.5499
}

Flags

name value
af_correlation FALSE
inflation_factor TRUE
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 FALSE
miss_EFFECT FALSE
miss_SE FALSE
miss_PVAL FALSE
ldsc_ratio TRUE
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 1.000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.886027e+07 5.628334e+07 828.000000 3.259061e+07 6.948835e+07 1.145912e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.090000e-04 8.424300e-03 -0.115964 -2.504000e-03 5.680000e-05 2.680000e-03 1.461230e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.601600e-03 5.304900e-03 0.001567 1.918400e-03 3.217000e-03 7.423000e-03 8.222670e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.626391e-01 2.977738e-01 0.000000 2.000000e-01 4.500005e-01 7.199992e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.626392e-01 2.977508e-01 0.000000 1.957973e-01 4.479286e-01 7.199433e-01 9.999999e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.035073e-01 2.568641e-01 0.000973 1.316800e-02 7.790800e-02 3.164540e-01 9.990220e-01 ▇▂▁▁▁
numeric AF_reference 184849 0.9812372 NA NA NA NA NA NA NA 2.068392e-01 2.482924e-01 0.000000 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 0.0007142 0.0028832 0.8000000 0.8043602 0.623850 0.7821490 NA
1 54676 rs2462492 C T -0.0047027 0.0028558 0.1000000 0.0996154 0.400418 NA NA
1 86028 rs114608975 T C 0.0072664 0.0045665 0.1100001 0.1115603 0.103536 0.0277556 NA
1 91536 rs6702460 G T -0.0032266 0.0028125 0.2500000 0.2512849 0.456940 0.4207270 NA
1 234313 rs8179466 C T 0.0021063 0.0055438 0.6999999 0.7039863 0.074528 NA NA
1 534192 rs6680723 C T 0.0065494 0.0032122 0.0409996 0.0414567 0.240962 NA NA
1 546697 rs12025928 A G -0.0059953 0.0040062 0.1299999 0.1345158 0.913432 NA NA
1 693731 rs12238997 A G 0.0052883 0.0026918 0.0490004 0.0494610 0.116295 0.1417730 NA
1 705882 rs72631875 G A -0.0049190 0.0039447 0.2099999 0.2124004 0.067308 0.0315495 NA
1 706368 rs55727773 A G -0.0026105 0.0019940 0.1900002 0.1904720 0.515682 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A 0.0021268 0.0041879 0.6100002 0.6115686 0.041946 0.0473243 NA
22 51219766 rs182321900 C T 0.0000536 0.0196058 1.0000000 0.9978188 0.001920 NA NA
22 51220146 rs868950473 C T -0.0001512 0.0194104 0.9900000 0.9937859 0.001970 NA NA
22 51221190 rs369304721 G A 0.0019347 0.0041815 0.6400000 0.6435933 0.049705 NA NA
22 51221731 rs115055839 T C 0.0034904 0.0031276 0.2599998 0.2644228 0.073195 0.0625000 NA
22 51222100 rs114553188 G T 0.0005536 0.0036825 0.8800001 0.8805080 0.054437 0.0880591 NA
22 51223637 rs375798137 G A 0.0004651 0.0037003 0.9000000 0.8999828 0.054067 0.0788738 NA
22 51229805 rs9616985 T C 0.0038305 0.0031389 0.2200002 0.2223385 0.073031 0.0730831 NA
22 51232488 rs376461333 A G -0.0023039 0.0073982 0.7600007 0.7554913 0.020020 NA NA
22 51237063 rs3896457 T C -0.0008548 0.0019191 0.6600001 0.6560257 0.298039 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62385  ES:SE:LP:AF:ID  0.000714202:0.00288324:0.09691:0.62385:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400418 ES:SE:LP:AF:ID  -0.00470269:0.00285579:1:0.400418:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103536 ES:SE:LP:AF:ID  0.00726635:0.00456653:0.958607:0.103536:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45694  ES:SE:LP:AF:ID  -0.00322663:0.00281253:0.60206:0.45694:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074528 ES:SE:LP:AF:ID  0.00210632:0.00554375:0.154902:0.074528:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240962 ES:SE:LP:AF:ID  0.00654944:0.00321219:1.38722:0.240962:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913432 ES:SE:LP:AF:ID  -0.00599532:0.00400615:0.886057:0.913432:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116295 ES:SE:LP:AF:ID  0.00528834:0.00269182:1.3098:0.116295:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067308 ES:SE:LP:AF:ID  -0.00491896:0.00394466:0.677781:0.067308:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515682 ES:SE:LP:AF:ID  -0.0026105:0.00199399:0.721246:0.515682:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032991 ES:SE:LP:AF:ID  0.00322308:0.00502728:0.283997:0.032991:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036606 ES:SE:LP:AF:ID  0.00227586:0.00456648:0.207608:0.036606:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036722 ES:SE:LP:AF:ID  0.00252288:0.00454924:0.236572:0.036722:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036424 ES:SE:LP:AF:ID  0.00300411:0.00458189:0.29243:0.036424:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016418 ES:SE:LP:AF:ID  0.0190533:0.00705042:2.16115:0.016418:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036962 ES:SE:LP:AF:ID  0.00245865:0.00453124:0.229148:0.036962:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037057 ES:SE:LP:AF:ID  0.00275528:0.00451578:0.267606:0.037057:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101236 ES:SE:LP:AF:ID  -0.000922858:0.00328857:0.107905:0.101236:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959129 ES:SE:LP:AF:ID  -0.00442571:0.00435596:0.508638:0.959129:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03144  ES:SE:LP:AF:ID  0.00134312:0.00790664:0.0604807:0.03144:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053272 ES:SE:LP:AF:ID  -0.00791202:0.00628482:0.677781:0.053272:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036576 ES:SE:LP:AF:ID  0.00284456:0.00454494:0.275724:0.036576:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036894 ES:SE:LP:AF:ID  0.00288175:0.00450356:0.283997:0.036894:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843274 ES:SE:LP:AF:ID  -0.00512535:0.0023328:1.55284:0.843274:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.05591  ES:SE:LP:AF:ID  0.00152769:0.00377729:0.161151:0.05591:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122272 ES:SE:LP:AF:ID  0.00530621:0.00255351:1.42022:0.122272:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025741 ES:SE:LP:AF:ID  0.0132805:0.00627691:1.46852:0.025741:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121516 ES:SE:LP:AF:ID  0.00526517:0.00255457:1.40894:0.121516:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132288 ES:SE:LP:AF:ID  0.00216296:0.00251791:0.408935:0.132288:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011113 ES:SE:LP:AF:ID  -0.00939114:0.00915951:0.508638:0.011113:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005687 ES:SE:LP:AF:ID  0.015293:0.0118292:0.69897:0.005687:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002262 ES:SE:LP:AF:ID  0.0494853:0.0199052:1.88606:0.002262:rs112573343
1   746189  rs139221807 A   G   .   PASS    AF=0.00102  ES:SE:LP:AF:ID  -0.0303134:0.0327728:0.455932:0.00102:rs139221807
1   752478  rs146277091 G   A   .   PASS    AF=0.036818 ES:SE:LP:AF:ID  0.00276246:0.00445735:0.267606:0.036818:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838997 ES:SE:LP:AF:ID  -0.00452493:0.00225915:1.34679:0.838997:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838629 ES:SE:LP:AF:ID  -0.00451657:0.00225677:1.34679:0.838629:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.86982  ES:SE:LP:AF:ID  -0.00531106:0.00242183:1.55284:0.86982:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129828 ES:SE:LP:AF:ID  0.00490923:0.00242684:1.36653:0.129828:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037325 ES:SE:LP:AF:ID  0.00226976:0.00438201:0.221849:0.037325:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037565 ES:SE:LP:AF:ID  0.00231389:0.00435463:0.221849:0.037565:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869166 ES:SE:LP:AF:ID  -0.00529829:0.00241715:1.55284:0.869166:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869264 ES:SE:LP:AF:ID  -0.00534165:0.00241809:1.56864:0.869264:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037528 ES:SE:LP:AF:ID  0.0026332:0.00437314:0.259637:0.037528:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869167 ES:SE:LP:AF:ID  -0.00531613:0.0024171:1.55284:0.869167:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00512  ES:SE:LP:AF:ID  -0.00248101:0.012412:0.0757207:0.00512:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005086 ES:SE:LP:AF:ID  -0.00211893:0.0124442:0.0655015:0.005086:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838078 ES:SE:LP:AF:ID  -0.00442832:0.00225045:1.3098:0.838078:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037538 ES:SE:LP:AF:ID  0.00258201:0.00437947:0.251812:0.037538:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838709 ES:SE:LP:AF:ID  -0.00441751:0.0022568:1.30103:0.838709:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013787 ES:SE:LP:AF:ID  -0.00613988:0.00787219:0.356547:0.013787:rs181660517