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/dev/ukb-d-import/processed/ukb-d-22617_3111/ukb-d-22617_3111.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-22617_3111/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 14:57:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22617_3111/ukb-d-22617_3111.vcf.gz ...
Read summary statistics for 10901508 SNPs.
Dropped 8924 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, 1281732 SNPs remain.
After merging with regression SNP LD, 1281732 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: -0.0005 (0.0052)
Lambda GC: 1.0002
Mean Chi^2: 1.0042
Intercept: 1.0051 (0.0063)
Ratio: 1.2296 (1.511)
Analysis finished at Mon Nov 25 14:59:01 2019
Total time elapsed: 1.0m:41.55s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9456,
    "inflation_factor": 0.993,
    "mean_EFFECT": -3.5076e-06,
    "n": 91149,
    "n_snps": 10901508,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 1133093,
    "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": 216905,
    "n_est": 91298.9116,
    "ratio_se_n": 1.0008,
    "mean_diff": -0,
    "ratio_diff": 31.1516,
    "sd_y_est1": 0.167,
    "sd_y_est2": 0.1672,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1281732,
    "ldsc_nsnp_merge_regression_ld": 1281732,
    "ldsc_observed_scale_h2_beta": "NA",
    "ldsc_observed_scale_h2_se": "NA",
    "ldsc_intercept_beta": 1.0051,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.0002,
    "ldsc_mean_chisq": 1.0042,
    "ldsc_ratio": 1.2143
}

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 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 numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 94 0 10893168 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 50817 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 31192 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.074008e+00 6.179836e+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 7.881135e+07 5.603285e+07 3.02000e+02 3.258760e+07 6.977957e+07 1.147403e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -3.500000e-06 2.338400e-03 -2.26119e-02 -9.553000e-04 -1.190000e-05 9.189000e-04 2.879600e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.906100e-03 1.357800e-03 6.46600e-04 8.779000e-04 1.273200e-03 2.540400e-03 8.075500e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 5.007891e-01 2.887109e-01 1.00000e-07 2.507160e-01 5.015001e-01 7.510848e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 5.007873e-01 2.887120e-01 1.00000e-07 2.507132e-01 5.014985e-01 7.510841e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.350593e-01 2.620068e-01 4.80400e-03 2.551140e-02 1.214580e-01 3.761560e-01 9.951940e-01 ▇▂▂▁▁
numeric AF_reference 216905 0.9801032 NA NA NA NA NA 2.351908e-01 2.530669e-01 0.00000e+00 2.476040e-02 1.387780e-01 3.744010e-01 1.000000e+00 ▇▂▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA 9.114900e+04 0.000000e+00 9.11490e+04 9.114900e+04 9.114900e+04 9.114900e+04 9.114900e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0003099 0.0013678 0.8207710 0.8207713 0.1114870 0.1894970 91149
1 693731 rs12238997 A G -0.0002453 0.0012954 0.8497880 0.8497876 0.1164590 0.1417730 91149
1 707522 rs371890604 G C 0.0002269 0.0014557 0.8761450 0.8761450 0.0978765 0.1293930 91149
1 717587 rs144155419 G A -0.0005246 0.0034702 0.8798291 0.8798287 0.0157837 0.0045926 91149
1 730087 rs148120343 T C 0.0006617 0.0017955 0.7124674 0.7124662 0.0569973 0.0127796 91149
1 731718 rs142557973 T C -0.0001750 0.0012293 0.8867859 0.8867857 0.1223590 0.1543530 91149
1 732032 rs61770163 A C -0.0005767 0.0013107 0.6599575 0.6599552 0.1220470 0.1555510 91149
1 734349 rs141242758 T C -0.0002128 0.0012302 0.8626380 0.8626379 0.1215880 0.1525560 91149
1 740284 rs61770167 C T 0.0058590 0.0056486 0.2996167 0.2996143 0.0057444 0.0023962 91149
1 749963 rs529266287 T TAA 0.0003964 0.0012101 0.7432604 0.7432594 0.8689170 0.7641770 91149
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0008399 0.0014390 0.5594574 0.5594557 0.0552869 0.0309934 91149
23 154923374 rs111332691 T A -0.0011936 0.0015660 0.4459313 0.4459274 0.0448387 0.0116556 91149
23 154925045 rs509981 C T -0.0000011 0.0007547 0.9987960 0.9987964 0.2444670 0.3634440 91149
23 154925895 rs538470 C T 0.0000800 0.0007716 0.9174229 0.9174228 0.2409490 0.3634440 91149
23 154927581 rs644138 G A -0.0001988 0.0007098 0.7794675 0.7794673 0.3001640 0.4635760 91149
23 154929412 rs557132 C T 0.0000140 0.0007549 0.9851720 0.9851720 0.2443160 0.3568210 91149
23 154929637 rs35185538 CT C -0.0001723 0.0007882 0.8269640 0.8269636 0.2286640 0.3011920 91149
23 154929952 rs4012982 CAA C -0.0002034 0.0007945 0.7979410 0.7979404 0.2382530 0.3165560 91149
23 154930230 rs781880 A G 0.0000182 0.0007547 0.9807980 0.9807983 0.2447660 0.3618540 91149
23 154930487 rs781879 T A 0.0018441 0.0025594 0.4712203 0.4712177 0.0196632 0.1263580 91149

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111487 ES:SE:LP:AF:SS:ID   0.000309868:0.00136775:0.085778:0.111487:91149:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116459 ES:SE:LP:AF:SS:ID   -0.000245335:0.0012954:0.0706894:0.116459:91149:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0978765    ES:SE:LP:AF:SS:ID   0.000226887:0.00145573:0.057424:0.0978765:91149:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157837    ES:SE:LP:AF:SS:ID   -0.000524641:0.00347016:0.0556017:0.0157837:91149:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0569973    ES:SE:LP:AF:SS:ID   0.000661728:0.00179551:0.147235:0.0569973:91149:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122359 ES:SE:LP:AF:SS:ID   -0.000175013:0.00122926:0.0521812:0.122359:91149:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122047 ES:SE:LP:AF:SS:ID   -0.000576662:0.00131067:0.180484:0.122047:91149:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121588 ES:SE:LP:AF:SS:ID   -0.000212844:0.00123019:0.0641714:0.121588:91149:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00574438   ES:SE:LP:AF:SS:ID   0.00585903:0.00564856:0.523434:0.00574438:91149:rs61770167
1   749963  rs529266287 T   TAA .   PASS    AF=0.868917 ES:SE:LP:AF:SS:ID   0.000396353:0.00121009:0.128859:0.868917:91149:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.123699 ES:SE:LP:AF:SS:ID   7.62451e-05:0.00119972:0.0225842:0.123699:91149:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143202 ES:SE:LP:AF:SS:ID   0.00105711:0.00118773:0.427765:0.143202:91149:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123834 ES:SE:LP:AF:SS:ID   7.39825e-05:0.00119798:0.0219305:0.123834:91149:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869892 ES:SE:LP:AF:SS:ID   -0.000250288:0.00116732:0.0808037:0.869892:91149:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874553 ES:SE:LP:AF:SS:ID   -0.00027633:0.00118625:0.0884136:0.874553:91149:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129529 ES:SE:LP:AF:SS:ID   0.00020084:0.00117098:0.0635772:0.129529:91149:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0358205    ES:SE:LP:AF:SS:ID   0.000656992:0.00213926:0.119896:0.0358205:91149:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869539 ES:SE:LP:AF:SS:ID   -0.000251148:0.00116634:0.0811779:0.869539:91149:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869616 ES:SE:LP:AF:SS:ID   -0.000271029:0.00116685:0.0881364:0.869616:91149:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869535 ES:SE:LP:AF:SS:ID   -0.00025193:0.00116629:0.0814554:0.869535:91149:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00531588   ES:SE:LP:AF:SS:ID   0.00318392:0.0058262:0.23304:0.00531588:91149:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00528125   ES:SE:LP:AF:SS:ID   0.00318471:0.00584068:0.232418:0.00528125:91149:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.0056599    ES:SE:LP:AF:SS:ID   0.00177093:0.00574171:0.120472:0.0056599:91149:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.86952  ES:SE:LP:AF:SS:ID   -0.000269261:0.00116389:0.087753:0.86952:91149:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126911 ES:SE:LP:AF:SS:ID   0.000100989:0.00118813:0.0304616:0.126911:91149:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.86905  ES:SE:LP:AF:SS:ID   -0.000334975:0.00116088:0.111863:0.86905:91149:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868436 ES:SE:LP:AF:SS:ID   -0.000344506:0.00116014:0.115486:0.868436:91149:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869199 ES:SE:LP:AF:SS:ID   -0.000332397:0.00116193:0.110798:0.869199:91149:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869205 ES:SE:LP:AF:SS:ID   -0.000332526:0.00116201:0.110837:0.869205:91149:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869207 ES:SE:LP:AF:SS:ID   -0.000328858:0.00116202:0.109483:0.869207:91149:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869687 ES:SE:LP:AF:SS:ID   -0.000289024:0.00116513:0.094697:0.869687:91149:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0991787    ES:SE:LP:AF:SS:ID   0.000538717:0.00135592:0.160433:0.0991787:91149:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00649624   ES:SE:LP:AF:SS:ID   0.000572585:0.00523103:0.0396063:0.00649624:91149:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873682 ES:SE:LP:AF:SS:ID   -0.000195954:0.00118238:0.0612947:0.873682:91149:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863058 ES:SE:LP:AF:SS:ID   -0.000287034:0.00115982:0.0944545:0.863058:91149:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868472 ES:SE:LP:AF:SS:ID   -0.000331785:0.00117133:0.10959:0.868472:91149:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865369 ES:SE:LP:AF:SS:ID   9.28816e-05:0.00117155:0.0283489:0.865369:91149:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0995228    ES:SE:LP:AF:SS:ID   0.000318102:0.00140212:0.0859087:0.0995228:91149:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870657 ES:SE:LP:AF:SS:ID   -6.54824e-05:0.00118489:0.0195748:0.870657:91149:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870657 ES:SE:LP:AF:SS:ID   -6.54637e-05:0.00118489:0.0195689:0.870657:91149:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870654 ES:SE:LP:AF:SS:ID   -6.51126e-05:0.0011849:0.0194617:0.870654:91149:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871019 ES:SE:LP:AF:SS:ID   -0.000106642:0.00118545:0.0323023:0.871019:91149:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126515 ES:SE:LP:AF:SS:ID   0.000110029:0.00118677:0.0333271:0.126515:91149:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106037 ES:SE:LP:AF:SS:ID   -2.34624e-05:0.00129002:0.00634822:0.106037:91149:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.85438  ES:SE:LP:AF:SS:ID   -9.10427e-05:0.00115938:0.0280709:0.85438:91149:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839153 ES:SE:LP:AF:SS:ID   -0.000686832:0.00114551:0.260598:0.839153:91149:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.763195 ES:SE:LP:AF:SS:ID   -0.000732769:0.000921051:0.370306:0.763195:91149:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.105092 ES:SE:LP:AF:SS:ID   0.00105522:0.00126922:0.39174:0.105092:91149:rs12562034
1   768819  rs12562811  C   T   .   PASS    AF=0.00772317   ES:SE:LP:AF:SS:ID   -0.00186912:0.0047029:0.160494:0.00772317:91149:rs12562811
1   769138  rs59306077  CAT C   .   PASS    AF=0.129904 ES:SE:LP:AF:SS:ID   0.000204866:0.00117019:0.0649842:0.129904:91149:rs762168062