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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}

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/dev/ukb-d-import/processed/ukb-d-1428_3/ukb-d-1428_3.vcf.gz \
--ref-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-1428_3/ldsc.txt \
--w-ld-chr /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/ 

Beginning analysis at Mon Nov 25 17:11:08 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-1428_3/ukb-d-1428_3.vcf.gz ...
Read summary statistics for 13586560 SNPs.
Dropped 12711 SNPs with duplicated rs numbers.
Reading reference panel LD Score from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/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 /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1283504 SNPs remain.
After merging with regression SNP LD, 1283504 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0311 (0.0018)
Lambda GC: 1.185
Mean Chi^2: 1.2132
Intercept: 0.9935 (0.0076)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 17:13:24 2019
Total time elapsed: 2.0m:16.76s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.1203,
    "mean_EFFECT": 0,
    "n": 360291,
    "n_snps": 13586560,
    "n_clumped_hits": 2,
    "n_p_sig": 37,
    "n_mono": 0,
    "n_ns": 1251807,
    "n_mac": 0,
    "is_snpid_unique": false,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 569286,
    "n_est": 361663.1296,
    "ratio_se_n": 1.0019,
    "mean_diff": 0,
    "ratio_diff": 6.0031,
    "sd_y_est1": 0.4985,
    "sd_y_est2": 0.4994,
    "r2_sum1": 0.0001,
    "r2_sum2": 0.0003,
    "r2_sum3": 0.0003,
    "r2_sum4": 0.0003,
    "ldsc_nsnp_merge_refpanel_ld": 1283504,
    "ldsc_nsnp_merge_regression_ld": 1283504,
    "ldsc_observed_scale_h2_beta": 0.0311,
    "ldsc_observed_scale_h2_se": 0.0018,
    "ldsc_intercept_beta": 0.9935,
    "ldsc_intercept_se": 0.0076,
    "ldsc_lambda_gc": 1.185,
    "ldsc_mean_chisq": 1.2132,
    "ldsc_ratio": -0.0305
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n FALSE
is_snpid_non_unique TRUE
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 numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 94 0 13574513 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 57083 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33337 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.051323e+00 6.184959e+00 1.00000e+00 4.000000e+00 8.000000e+00 1.300000e+01 2.300000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA 7.902515e+07 5.591485e+07 3.02000e+02 3.293104e+07 7.013866e+07 1.148591e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 1.570000e-05 6.956300e-03 -9.19547e-02 -1.925500e-03 1.260000e-05 1.950800e-03 8.552780e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 4.934800e-03 4.760300e-03 9.65500e-04 1.404900e-03 2.589000e-03 7.092000e-03 2.392860e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.817773e-01 2.938559e-01 0.00000e+00 2.228820e-01 4.752936e-01 7.368804e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.817768e-01 2.938562e-01 0.00000e+00 2.228812e-01 4.752940e-01 7.368800e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.908021e-01 2.544023e-01 1.00000e-03 7.429100e-03 6.003080e-02 2.953072e-01 9.989990e-01 ▇▂▁▁▁
numeric AF_reference 569286 0.9580993 NA NA NA NA NA 1.963332e-01 2.471007e-01 0.00000e+00 5.591000e-03 8.426520e-02 3.081070e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 3.602910e+05 0.000000e+00 3.60291e+05 3.602910e+05 3.602910e+05 3.602910e+05 3.602910e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0017141 0.0020564 0.4045368 0.4045357 0.1106410 0.1894970 360291
1 693731 rs12238997 A G -0.0008616 0.0019430 0.6574610 0.6574601 0.1158290 0.1417730 360291
1 707522 rs371890604 G C -0.0007634 0.0021845 0.7267315 0.7267302 0.0972946 0.1293930 360291
1 717587 rs144155419 G A -0.0065205 0.0052138 0.2110770 0.2110756 0.0156835 0.0045926 360291
1 723329 rs189787166 A T 0.0076885 0.0153823 0.6171987 0.6171978 0.0017332 0.0003994 360291
1 730087 rs148120343 T C -0.0004683 0.0027070 0.8626670 0.8626675 0.0564617 0.0127796 360291
1 731718 rs142557973 T C -0.0010975 0.0018427 0.5514486 0.5514483 0.1217350 0.1543530 360291
1 732032 rs61770163 A C -0.0014637 0.0019658 0.4565339 0.4565327 0.1211610 0.1555510 360291
1 734349 rs141242758 T C -0.0010883 0.0018437 0.5550064 0.5550070 0.1209640 0.1525560 360291
1 740284 rs61770167 C T -0.0004498 0.0084344 0.9574740 0.9574737 0.0057870 0.0023962 360291
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0004494 0.0021294 0.8328491 0.8328486 0.0561529 0.0309934 360291
23 154923374 rs111332691 T A -0.0015780 0.0023405 0.5001601 0.5001583 0.0447735 0.0116556 360291
23 154925045 rs509981 C T -0.0011575 0.0011258 0.3038939 0.3038929 0.2456550 0.3634440 360291
23 154925895 rs538470 C T -0.0009633 0.0011517 0.4029229 0.4029215 0.2419630 0.3634440 360291
23 154927581 rs644138 G A -0.0011851 0.0010588 0.2630323 0.2630302 0.3021960 0.4635760 360291
23 154929412 rs557132 C T -0.0011492 0.0011262 0.3075112 0.3075110 0.2455070 0.3568210 360291
23 154929637 rs35185538 CT C -0.0015304 0.0011753 0.1928928 0.1928921 0.2297500 0.3011920 360291
23 154929952 rs4012982 CAA C -0.0007406 0.0011844 0.5318057 0.5318041 0.2394730 0.3165560 360291
23 154930230 rs781880 A G -0.0011011 0.0011260 0.3281452 0.3281437 0.2459170 0.3618540 360291
23 154930487 rs781879 T A 0.0026526 0.0038628 0.4922650 0.4922649 0.0195664 0.1263580 360291

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.110641 ES:SE:LP:AF:SS:ID   -0.0017141:0.00205639:0.393042:0.110641:360291:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.115829 ES:SE:LP:AF:SS:ID   -0.000861579:0.00194302:0.18213:0.115829:360291:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0972946    ES:SE:LP:AF:SS:ID   -0.000763446:0.00218453:0.138626:0.0972946:360291:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0156835    ES:SE:LP:AF:SS:ID   -0.00652051:0.00521385:0.675559:0.0156835:360291:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00173323   ES:SE:LP:AF:SS:ID   0.00768847:0.0153823:0.209575:0.00173323:360291:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0564617    ES:SE:LP:AF:SS:ID   -0.000468259:0.00270702:0.0641568:0.0564617:360291:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121735 ES:SE:LP:AF:SS:ID   -0.00109752:0.00184274:0.258495:0.121735:360291:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121161 ES:SE:LP:AF:SS:ID   -0.00146368:0.00196581:0.340527:0.121161:360291:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.120964 ES:SE:LP:AF:SS:ID   -0.0010883:0.00184372:0.255702:0.120964:360291:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00578701   ES:SE:LP:AF:SS:ID   -0.000449756:0.00843439:0.018873:0.00578701:360291:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00187727   ES:SE:LP:AF:SS:ID   0.0249315:0.0159341:0.92936:0.00187727:360291:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869752 ES:SE:LP:AF:SS:ID   0.000861416:0.00181773:0.196833:0.869752:360291:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00152845   ES:SE:LP:AF:SS:ID   0.0083213:0.0164679:0.212295:0.00152845:360291:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.122915 ES:SE:LP:AF:SS:ID   -0.00106282:0.00180021:0.255762:0.122915:360291:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142717 ES:SE:LP:AF:SS:ID   -0.000337763:0.0017794:0.0708617:0.142717:360291:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123031 ES:SE:LP:AF:SS:ID   -0.00117709:0.0017977:0.290212:0.123031:360291:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.870894 ES:SE:LP:AF:SS:ID   0.00124075:0.00175183:0.319858:0.870894:360291:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.875476 ES:SE:LP:AF:SS:ID   0.00136547:0.00178088:0.353363:0.875476:360291:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128629 ES:SE:LP:AF:SS:ID   -0.0013072:0.00175618:0.340398:0.128629:360291:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.036347 ES:SE:LP:AF:SS:ID   -0.00128145:0.00318683:0.16266:0.036347:360291:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870487 ES:SE:LP:AF:SS:ID   0.00129181:0.00175001:0.336857:0.870487:360291:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870591 ES:SE:LP:AF:SS:ID   0.00128023:0.0017508:0.332882:0.870591:360291:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870484 ES:SE:LP:AF:SS:ID   0.00129359:0.00174994:0.337457:0.870484:360291:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00504876   ES:SE:LP:AF:SS:ID   -0.00264178:0.00897915:0.114302:0.00504876:360291:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.0050157    ES:SE:LP:AF:SS:ID   -0.00251715:0.00900319:0.108019:0.0050157:360291:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00558084   ES:SE:LP:AF:SS:ID   0.0133572:0.00870644:0.903132:0.00558084:360291:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870567 ES:SE:LP:AF:SS:ID   0.00131367:0.00174687:0.34482:0.870567:360291:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.125982 ES:SE:LP:AF:SS:ID   -0.00116812:0.0017824:0.290531:0.125982:360291:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.87013  ES:SE:LP:AF:SS:ID   0.00134809:0.00174266:0.357359:0.87013:360291:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869418 ES:SE:LP:AF:SS:ID   0.00126688:0.00174106:0.33084:0.869418:360291:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870285 ES:SE:LP:AF:SS:ID   0.00129658:0.00174416:0.339848:0.870285:360291:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870291 ES:SE:LP:AF:SS:ID   0.001298:0.00174428:0.340283:0.870291:360291:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870299 ES:SE:LP:AF:SS:ID   0.00128926:0.00174434:0.337394:0.870299:360291:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870743 ES:SE:LP:AF:SS:ID   0.00131961:0.00174878:0.346308:0.870743:360291:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0988398    ES:SE:LP:AF:SS:ID   -0.000409417:0.00203225:0.0755455:0.0988398:360291:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00647113   ES:SE:LP:AF:SS:ID   0.00666908:0.007864:0.401855:0.00647113:360291:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.874641 ES:SE:LP:AF:SS:ID   0.00134909:0.00177487:0.349505:0.874641:360291:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.864033 ES:SE:LP:AF:SS:ID   0.0011373:0.00174048:0.289482:0.864033:360291:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.869431 ES:SE:LP:AF:SS:ID   0.00136388:0.00175739:0.35882:0.869431:360291:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866373 ES:SE:LP:AF:SS:ID   0.00146763:0.00175775:0.393893:0.866373:360291:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.098771 ES:SE:LP:AF:SS:ID   -0.00258028:0.00210452:0.657234:0.098771:360291:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.87156  ES:SE:LP:AF:SS:ID   0.00115378:0.0017778:0.287063:0.87156:360291:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.87156  ES:SE:LP:AF:SS:ID   0.00115375:0.0017778:0.287051:0.87156:360291:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.87156  ES:SE:LP:AF:SS:ID   0.00116144:0.00177784:0.289398:0.87156:360291:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871934 ES:SE:LP:AF:SS:ID   0.00126013:0.00177885:0.319936:0.871934:360291:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125558 ES:SE:LP:AF:SS:ID   -0.00137249:0.0017802:0.355833:0.125558:360291:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.10543  ES:SE:LP:AF:SS:ID   -0.000661562:0.00193802:0.134995:0.10543:360291:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855385 ES:SE:LP:AF:SS:ID   0.00158331:0.00173984:0.440323:0.855385:360291:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00165413   ES:SE:LP:AF:SS:ID   0.00961796:0.0154154:0.273533:0.00165413:360291:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838383 ES:SE:LP:AF:SS:ID   0.00108601:0.00171421:0.278696:0.838383:360291:rs376645387