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-9554/UKB-b-9554_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9554/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:46 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-9554/UKB-b-9554_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: 0.0005 (0.001)
Lambda GC: 1.0564
Mean Chi^2: 1.0585
Intercept: 1.0542 (0.0062)
Ratio: 0.9276 (0.1066)
Analysis finished at Thu Oct 17 14:44:32 2019
Total time elapsed: 1.0m:45.67s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9499,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 0.0002,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 1,
    "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": 0.0005,
    "ldsc_observed_scale_h2_se": 0.001,
    "ldsc_intercept_beta": 1.0542,
    "ldsc_intercept_se": 0.0062,
    "ldsc_lambda_gc": 1.0564,
    "ldsc_mean_chisq": 1.0585,
    "ldsc_ratio": 0.9265
}

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 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 1.0000000 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 1.554000e-04 5.633700e-03 -0.1303850 -1.605400e-03 2.290000e-05 1.692700e-03 8.720230e-02 ▁▁▇▇▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 3.937700e-03 3.727600e-03 0.0011017 1.348800e-03 2.261700e-03 5.218800e-03 5.794250e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.913369e-01 2.908470e-01 0.0000000 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 4.913376e-01 2.908212e-01 0.0000000 2.371286e-01 4.884913e-01 7.429563e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.035075e-01 2.568621e-01 0.0009790 1.316800e-02 7.791200e-02 3.164550e-01 9.990120e-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 -0.0011639 0.0020273 0.5700002 0.5658907 0.623756 0.7821490 NA
1 54676 rs2462492 C T 0.0023175 0.0020089 0.2500000 0.2486598 0.400407 NA NA
1 86028 rs114608975 T C 0.0001456 0.0032120 0.9599999 0.9638491 0.103555 0.0277556 NA
1 91536 rs6702460 G T 0.0048235 0.0019776 0.0150000 0.0147265 0.456805 0.4207270 NA
1 234313 rs8179466 C T -0.0038002 0.0038981 0.3300000 0.3296109 0.074538 NA NA
1 534192 rs6680723 C T -0.0029519 0.0022588 0.1900002 0.1912730 0.240997 NA NA
1 546697 rs12025928 A G 0.0046858 0.0028194 0.0969996 0.0965163 0.913520 NA NA
1 693731 rs12238997 A G 0.0015073 0.0018926 0.4299995 0.4257808 0.116381 0.1417730 NA
1 705882 rs72631875 G A -0.0026009 0.0027758 0.3500000 0.3487688 0.067232 0.0315495 NA
1 706368 rs55727773 A G -0.0018062 0.0014027 0.2000000 0.1978532 0.515569 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A 0.0041808 0.0029440 0.1600000 0.1555860 0.041920 0.0473243 NA
22 51219766 rs182321900 C T 0.0326529 0.0137262 0.0170000 0.0173656 0.001934 NA NA
22 51220146 rs868950473 C T 0.0333018 0.0136000 0.0140001 0.0143388 0.001981 NA NA
22 51221190 rs369304721 G A 0.0051516 0.0029397 0.0800000 0.0796978 0.049688 NA NA
22 51221731 rs115055839 T C 0.0024888 0.0021979 0.2599998 0.2574733 0.073200 0.0625000 NA
22 51222100 rs114553188 G T 0.0003236 0.0025869 0.9000000 0.9004659 0.054461 0.0880591 NA
22 51223637 rs375798137 G A 0.0004346 0.0025994 0.8700001 0.8672221 0.054091 0.0788738 NA
22 51229805 rs9616985 T C 0.0023534 0.0022058 0.2900000 0.2860068 0.073035 0.0730831 NA
22 51232488 rs376461333 A G -0.0000343 0.0051952 0.9900000 0.9947245 0.020045 NA NA
22 51237063 rs3896457 T C -0.0018939 0.0013487 0.1600000 0.1602528 0.297983 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623756 ES:SE:LP:AF:ID  -0.00116393:0.00202735:0.244125:0.623756:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400407 ES:SE:LP:AF:ID  0.0023175:0.00200891:0.60206:0.400407:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103555 ES:SE:LP:AF:ID  0.000145582:0.00321203:0.0177288:0.103555:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456805 ES:SE:LP:AF:ID  0.00482351:0.00197763:1.82391:0.456805:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074538 ES:SE:LP:AF:ID  -0.00380023:0.00389808:0.481486:0.074538:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240997 ES:SE:LP:AF:ID  -0.00295189:0.00225883:0.721246:0.240997:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.91352  ES:SE:LP:AF:ID  0.00468581:0.00281941:1.01323:0.91352:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116381 ES:SE:LP:AF:ID  0.00150732:0.00189259:0.366532:0.116381:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067232 ES:SE:LP:AF:ID  -0.00260086:0.00277579:0.455932:0.067232:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515569 ES:SE:LP:AF:ID  -0.00180622:0.00140268:0.69897:0.515569:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033    ES:SE:LP:AF:ID  0.00679943:0.00353575:1.26761:0.033:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036611 ES:SE:LP:AF:ID  0.00628175:0.00321179:1.30103:0.036611:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036726 ES:SE:LP:AF:ID  0.00642725:0.00319967:1.34679:0.036726:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036422 ES:SE:LP:AF:ID  0.00628244:0.00322289:1.29243:0.036422:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.01643  ES:SE:LP:AF:ID  0.00431651:0.00495716:0.420216:0.01643:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036964 ES:SE:LP:AF:ID  0.00658832:0.00318703:1.40894:0.036964:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037061 ES:SE:LP:AF:ID  0.00591693:0.00317612:1.20761:0.037061:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101221 ES:SE:LP:AF:ID  -0.00380863:0.00231375:1:0.101221:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959116 ES:SE:LP:AF:ID  -0.00743091:0.00306359:1.82391:0.959116:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031451 ES:SE:LP:AF:ID  -4.67768e-05:0.00555852:0.00436481:0.031451:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053252 ES:SE:LP:AF:ID  -0.00657743:0.00442392:0.853872:0.053252:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036579 ES:SE:LP:AF:ID  0.00608282:0.00319673:1.24413:0.036579:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.0369   ES:SE:LP:AF:ID  0.00624598:0.00316744:1.3098:0.0369:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843173 ES:SE:LP:AF:ID  -0.0033652:0.00164036:1.39794:0.843173:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055948 ES:SE:LP:AF:ID  0.00299003:0.00265545:0.585027:0.055948:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12237  ES:SE:LP:AF:ID  0.002428:0.00179527:0.744727:0.12237:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025756 ES:SE:LP:AF:ID  -0.000143736:0.00441362:0.0132283:0.025756:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121613 ES:SE:LP:AF:ID  0.00244264:0.001796:0.769551:0.121613:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132352 ES:SE:LP:AF:ID  0.00326204:0.00177038:1.18709:0.132352:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011107 ES:SE:LP:AF:ID  0.00306118:0.00644804:0.200659:0.011107:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005704 ES:SE:LP:AF:ID  0.0139422:0.00830688:1.03152:0.005704:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002262 ES:SE:LP:AF:ID  0.0215818:0.0139942:0.920819:0.002262:rs112573343
1   746189  rs139221807 A   G   .   PASS    AF=0.001028 ES:SE:LP:AF:ID  0.0213216:0.0229108:0.455932:0.001028:rs139221807
1   752478  rs146277091 G   A   .   PASS    AF=0.036814 ES:SE:LP:AF:ID  0.00630434:0.00313541:1.35655:0.036814:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838895 ES:SE:LP:AF:ID  -0.00368965:0.0015885:1.69897:0.838895:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838528 ES:SE:LP:AF:ID  -0.00363881:0.00158681:1.65758:0.838528:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869711 ES:SE:LP:AF:ID  -0.00287002:0.00170253:1.03621:0.869711:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129936 ES:SE:LP:AF:ID  0.00282625:0.00170604:1.00877:0.129936:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037328 ES:SE:LP:AF:ID  0.00583045:0.00308205:1.22915:0.037328:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037572 ES:SE:LP:AF:ID  0.00579982:0.00306253:1.23657:0.037572:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869056 ES:SE:LP:AF:ID  -0.00279672:0.00169923:1:0.869056:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869154 ES:SE:LP:AF:ID  -0.00265652:0.00169991:0.920819:0.869154:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037531 ES:SE:LP:AF:ID  0.00586818:0.00307578:1.25181:0.037531:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86906  ES:SE:LP:AF:ID  -0.0027928:0.00169921:1:0.86906:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005128 ES:SE:LP:AF:ID  0.011721:0.0087209:0.744727:0.005128:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005094 ES:SE:LP:AF:ID  0.0113356:0.0087439:0.721246:0.005094:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837976 ES:SE:LP:AF:ID  -0.0035688:0.00158235:1.61979:0.837976:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037544 ES:SE:LP:AF:ID  0.00588008:0.00308013:1.25181:0.037544:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838606 ES:SE:LP:AF:ID  -0.00353196:0.00158679:1.58503:0.838606:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013775 ES:SE:LP:AF:ID  -0.00242695:0.0055391:0.180456:0.013775:rs181660517