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-6222/UKB-b-6222_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6222/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6222/UKB-b-6222_data.vcf.gz ...
Read summary statistics for 8988558 SNPs.
Dropped 8675 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, 1287095 SNPs remain.
After merging with regression SNP LD, 1287095 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0179 (0.0014)
Lambda GC: 1.1608
Mean Chi^2: 1.1792
Intercept: 1.0136 (0.0074)
Ratio: 0.076 (0.0416)
Analysis finished at Thu Oct 17 14:42:00 2019
Total time elapsed: 1.0m:41.12s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9476,
    "inflation_factor": 1.0966,
    "mean_EFFECT": 6.4063e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 2,
    "n_p_sig": 2,
    "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": 92999,
    "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": 1287095,
    "ldsc_nsnp_merge_regression_ld": 1287095,
    "ldsc_observed_scale_h2_beta": 0.0179,
    "ldsc_observed_scale_h2_se": 0.0014,
    "ldsc_intercept_beta": 1.0136,
    "ldsc_intercept_se": 0.0074,
    "ldsc_lambda_gc": 1.1608,
    "ldsc_mean_chisq": 1.1792,
    "ldsc_ratio": 0.0759
}

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 8979922 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 8988558 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 8.643885e+00 5.758227e+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 7.878920e+07 5.633753e+07 828.0000000 3.243195e+07 6.935578e+07 1.145443e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 6.400000e-06 2.833300e-03 -0.0306649 -1.140200e-03 5.200000e-06 1.145700e-03 3.066500e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.230400e-03 1.629200e-03 0.0008139 9.696000e-04 1.482500e-03 3.057600e-03 1.887410e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.818352e-01 2.938238e-01 0.0000000 2.200002e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▆▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.818360e-01 2.938004e-01 0.0000000 2.226552e-01 4.755488e-01 7.365407e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.205877e-01 2.585930e-01 0.0036330 2.069300e-02 1.020310e-01 3.480430e-01 9.963670e-01 ▇▂▁▁▁
numeric AF_reference 92999 0.9896536 NA NA NA NA NA NA NA 2.208373e-01 2.504950e-01 0.0000000 1.797120e-02 1.194090e-01 3.462460e-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 -0.0013015 0.0014976 0.3800004 0.3847927 0.623765 0.7821490 NA
1 54676 rs2462492 C T 0.0018386 0.0014836 0.2200002 0.2152420 0.400401 NA NA
1 86028 rs114608975 T C -0.0006528 0.0023720 0.7800007 0.7831498 0.103556 0.0277556 NA
1 91536 rs6702460 G T 0.0009711 0.0014608 0.5099998 0.5061792 0.456846 0.4207270 NA
1 234313 rs8179466 C T 0.0007600 0.0028804 0.7899998 0.7918916 0.074506 NA NA
1 534192 rs6680723 C T 0.0004601 0.0016686 0.7800007 0.7827309 0.240959 NA NA
1 546697 rs12025928 A G 0.0026530 0.0020817 0.2000000 0.2025022 0.913475 NA NA
1 693731 rs12238997 A G 0.0000372 0.0013984 0.9800000 0.9787622 0.116329 0.1417730 NA
1 705882 rs72631875 G A -0.0016752 0.0020491 0.4100001 0.4136466 0.067288 0.0315495 NA
1 706368 rs55727773 A G -0.0011750 0.0010358 0.2599998 0.2566296 0.515645 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0007840 0.0012498 0.5300002 0.5304415 0.137950 0.2052720 NA
22 51219387 rs9616832 T C 0.0002483 0.0016222 0.8800001 0.8783563 0.073744 0.0654952 NA
22 51219704 rs147475742 G A 0.0020987 0.0021739 0.3300000 0.3343453 0.041954 0.0473243 NA
22 51221190 rs369304721 G A 0.0004928 0.0021703 0.8200001 0.8203657 0.049731 NA NA
22 51221731 rs115055839 T C 0.0003042 0.0016233 0.8499999 0.8513499 0.073235 0.0625000 NA
22 51222100 rs114553188 G T 0.0020057 0.0019111 0.2900000 0.2939476 0.054460 0.0880591 NA
22 51223637 rs375798137 G A 0.0019548 0.0019203 0.3100002 0.3087041 0.054089 0.0788738 NA
22 51229805 rs9616985 T C 0.0003472 0.0016291 0.8300000 0.8312215 0.073071 0.0730831 NA
22 51232488 rs376461333 A G -0.0001111 0.0038379 0.9800000 0.9769006 0.020042 NA NA
22 51237063 rs3896457 T C 0.0011530 0.0009964 0.2500000 0.2472032 0.297974 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623765 ES:SE:LP:AF:ID  -0.00130152:0.00149755:0.420216:0.623765:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  0.00183862:0.00148362:0.657577:0.400401:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103556 ES:SE:LP:AF:ID  -0.000652811:0.00237199:0.107905:0.103556:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456846 ES:SE:LP:AF:ID  0.000971138:0.0014608:0.29243:0.456846:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074506 ES:SE:LP:AF:ID  0.000759995:0.00288035:0.102373:0.074506:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240959 ES:SE:LP:AF:ID  0.000460142:0.00166862:0.107905:0.240959:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913475 ES:SE:LP:AF:ID  0.00265302:0.00208169:0.69897:0.913475:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116329 ES:SE:LP:AF:ID  3.72252e-05:0.00139835:0.00877392:0.116329:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067288 ES:SE:LP:AF:ID  -0.00167515:0.00204913:0.387216:0.067288:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515645 ES:SE:LP:AF:ID  -0.00117505:0.00103584:0.585027:0.515645:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033009 ES:SE:LP:AF:ID  -0.000308955:0.00261127:0.0409586:0.033009:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036624 ES:SE:LP:AF:ID  -1.69779e-05:0.00237194:0.00436481:0.036624:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036741 ES:SE:LP:AF:ID  -0.000294216:0.00236297:0.0457575:0.036741:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036439 ES:SE:LP:AF:ID  -0.000381076:0.00238002:0.0604807:0.036439:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016409 ES:SE:LP:AF:ID  0.00683293:0.00366457:1.20761:0.016409:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03698  ES:SE:LP:AF:ID  -0.000443643:0.0023536:0.0705811:0.03698:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037077 ES:SE:LP:AF:ID  -0.000275864:0.00234554:0.0409586:0.037077:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.1012   ES:SE:LP:AF:ID  0.00225612:0.00170908:0.721246:0.1012:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959091 ES:SE:LP:AF:ID  0.000632271:0.00226222:0.107905:0.959091:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03145  ES:SE:LP:AF:ID  -0.00513423:0.00410705:0.677781:0.03145:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053255 ES:SE:LP:AF:ID  -0.00121455:0.00326708:0.148742:0.053255:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036594 ES:SE:LP:AF:ID  -0.00086306:0.00236075:0.148742:0.036594:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03691  ES:SE:LP:AF:ID  -0.000478799:0.00233926:0.0757207:0.03691:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843204 ES:SE:LP:AF:ID  0.000248939:0.00121185:0.0757207:0.843204:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055912 ES:SE:LP:AF:ID  -0.00177915:0.00196219:0.443698:0.055912:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122312 ES:SE:LP:AF:ID  0.000535186:0.00132648:0.161151:0.122312:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025711 ES:SE:LP:AF:ID  -0.00047441:0.00326302:0.0555173:0.025711:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121554 ES:SE:LP:AF:ID  0.000437749:0.00132703:0.130768:0.121554:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132335 ES:SE:LP:AF:ID  -0.00104976:0.00130793:0.376751:0.132335:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011133 ES:SE:LP:AF:ID  -0.00124927:0.0047557:0.102373:0.011133:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005698 ES:SE:LP:AF:ID  -0.00471036:0.00613973:0.356547:0.005698:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036825 ES:SE:LP:AF:ID  -3.27626e-05:0.0023156:0.00436481:0.036825:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838945 ES:SE:LP:AF:ID  0.000224957:0.0011736:0.0705811:0.838945:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838573 ES:SE:LP:AF:ID  7.57118e-05:0.00117234:0.0222764:0.838573:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869776 ES:SE:LP:AF:ID  -4.94228e-05:0.00125796:0.0132283:0.869776:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129876 ES:SE:LP:AF:ID  0.000332137:0.00126053:0.102373:0.129876:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037336 ES:SE:LP:AF:ID  -5.34567e-05:0.00227634:0.00877392:0.037336:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.03758  ES:SE:LP:AF:ID  -7.25543e-05:0.00226195:0.0132283:0.03758:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869117 ES:SE:LP:AF:ID  -0.000170909:0.00125549:0.05061:0.869117:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869215 ES:SE:LP:AF:ID  -0.000161634:0.00125599:0.0457575:0.869215:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037538 ES:SE:LP:AF:ID  -5.66373e-05:0.00227174:0.00877392:0.037538:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869121 ES:SE:LP:AF:ID  -0.000176735:0.00125547:0.05061:0.869121:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005121 ES:SE:LP:AF:ID  0.00523097:0.00644678:0.376751:0.005121:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005087 ES:SE:LP:AF:ID  0.00521451:0.00646369:0.376751:0.005087:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838026 ES:SE:LP:AF:ID  0.000235709:0.00116908:0.0757207:0.838026:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.03755  ES:SE:LP:AF:ID  5.16979e-06:0.00227495:-0:0.03755:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838657 ES:SE:LP:AF:ID  0.000284756:0.00117237:0.091515:0.838657:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013772 ES:SE:LP:AF:ID  -0.00327105:0.00409257:0.376751:0.013772:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005547 ES:SE:LP:AF:ID  -0.00376722:0.0063143:0.259637:0.005547:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.83977  ES:SE:LP:AF:ID  0.000145076:0.00118822:0.0457575:0.83977:rs3131965