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-30090_irnt/ukb-d-30090_irnt.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-30090_irnt/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 15:34:29 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-30090_irnt/ukb-d-30090_irnt.vcf.gz ...
Read summary statistics for 13586284 SNPs.
Dropped 12710 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: 0.2114 (0.0194)
Lambda GC: 1.6423
Mean Chi^2: 2.6293
Intercept: 1.2185 (0.0209)
Ratio: 0.1341 (0.0128)
Analysis finished at Mon Nov 25 15:36:51 2019
Total time elapsed: 2.0m:21.94s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.4049,
    "mean_EFFECT": 0,
    "n": 350471,
    "n_snps": 13586284,
    "n_clumped_hits": 414,
    "n_p_sig": 84415,
    "n_mono": 0,
    "n_ns": 1251793,
    "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": 569192,
    "n_est": 351804.3135,
    "ratio_se_n": 1.0019,
    "mean_diff": 2.4594e-06,
    "ratio_diff": 1.3051,
    "sd_y_est1": 0.9541,
    "sd_y_est2": 0.9559,
    "r2_sum1": 0.1153,
    "r2_sum2": 0.1267,
    "r2_sum3": 0.1262,
    "r2_sum4": 0.1262,
    "ldsc_nsnp_merge_refpanel_ld": 1283504,
    "ldsc_nsnp_merge_regression_ld": 1283504,
    "ldsc_observed_scale_h2_beta": 0.2114,
    "ldsc_observed_scale_h2_se": 0.0194,
    "ldsc_intercept_beta": 1.2185,
    "ldsc_intercept_se": 0.0209,
    "ldsc_lambda_gc": 1.6423,
    "ldsc_mean_chisq": 2.6293,
    "ldsc_ratio": 0.1341
}

Flags

name value
af_correlation FALSE
inflation_factor TRUE
n FALSE
is_snpid_non_unique TRUE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq TRUE
n_p_sig TRUE
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 13574238 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 57080 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 9.051336e+00 6.184983e+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 7.902521e+07 5.591483e+07 3.02000e+02 3.293109e+07 7.013884e+07 1.148590e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 1.250000e-05 1.450620e-02 -3.55423e-01 -4.423100e-03 -1.090000e-05 4.394000e-03 7.102260e-01 ▁▇▁▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 9.576400e-03 9.237700e-03 1.87310e-03 2.726400e-03 5.024200e-03 1.376240e-02 4.683270e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.430652e-01 3.049214e-01 0.00000e+00 1.647791e-01 4.240265e-01 7.069944e-01 1.000000e+00 ▇▆▅▅▅
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.430648e-01 3.049216e-01 0.00000e+00 1.647783e-01 4.240257e-01 7.069943e-01 9.999998e-01 ▇▆▅▅▅
numeric AF 0 1.0000000 NA NA NA NA NA 1.908060e-01 2.544034e-01 1.00000e-03 7.430500e-03 6.003400e-02 2.953172e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 569192 0.9581054 NA NA NA NA NA 1.963357e-01 2.471015e-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 3.504710e+05 0.000000e+00 3.50471e+05 3.504710e+05 3.504710e+05 3.504710e+05 3.504710e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0044134 0.0039896 0.2686322 0.2686318 0.1106750 0.1894970 350471
1 693731 rs12238997 A G 0.0037245 0.0037697 0.3231469 0.3231467 0.1158560 0.1417730 350471
1 707522 rs371890604 G C 0.0034838 0.0042393 0.4111952 0.4111948 0.0972819 0.1293930 350471
1 717587 rs144155419 G A -0.0006255 0.0101040 0.9506389 0.9506386 0.0157162 0.0045926 350471
1 723329 rs189787166 A T -0.0091420 0.0299427 0.7601267 0.7601264 0.0017252 0.0003994 350471
1 730087 rs148120343 T C 0.0067346 0.0052501 0.1995782 0.1995763 0.0564929 0.0127796 350471
1 731718 rs142557973 T C 0.0035906 0.0035750 0.3152071 0.3152061 0.1217780 0.1543530 350471
1 732032 rs61770163 A C 0.0021274 0.0038140 0.5769976 0.5769966 0.1211730 0.1555510 350471
1 734349 rs141242758 T C 0.0036008 0.0035770 0.3140943 0.3140932 0.1210070 0.1525560 350471
1 740284 rs61770167 C T 0.0009849 0.0163667 0.9520130 0.9520129 0.0057859 0.0023962 350471
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0081693 0.0041294 0.0478906 0.0478898 0.0561537 0.0309934 350471
23 154923374 rs111332691 T A 0.0134144 0.0045393 0.0031254 0.0031252 0.0447912 0.0116556 350471
23 154925045 rs509981 C T -0.0019385 0.0021845 0.3748711 0.3748704 0.2454610 0.3634440 350471
23 154925895 rs538470 C T -0.0017100 0.0022344 0.4440839 0.4440821 0.2418090 0.3634440 350471
23 154927581 rs644138 G A 0.0002658 0.0020541 0.8970491 0.8970487 0.3020110 0.4635760 350471
23 154929412 rs557132 C T -0.0018486 0.0021851 0.3975539 0.3975553 0.2453160 0.3568210 350471
23 154929637 rs35185538 CT C 0.0006343 0.0022808 0.7809298 0.7809294 0.2295380 0.3011920 350471
23 154929952 rs4012982 CAA C -0.0019911 0.0022983 0.3863047 0.3863045 0.2392710 0.3165560 350471
23 154930230 rs781880 A G -0.0017485 0.0021848 0.4235191 0.4235199 0.2457270 0.3618540 350471
23 154930487 rs781879 T A -0.0070316 0.0074974 0.3483060 0.3483049 0.0195298 0.1263580 350471

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.110675 ES:SE:LP:AF:SS:ID   0.00441336:0.00398959:0.570842:0.110675:350471:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.115856 ES:SE:LP:AF:SS:ID   0.00372449:0.00376968:0.4906:0.115856:350471:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0972819    ES:SE:LP:AF:SS:ID   0.00348381:0.00423928:0.385952:0.0972819:350471:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157162    ES:SE:LP:AF:SS:ID   -0.000625487:0.010104:0.0219844:0.0157162:350471:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.0017252    ES:SE:LP:AF:SS:ID   -0.00914195:0.0299427:0.119114:0.0017252:350471:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0564929    ES:SE:LP:AF:SS:ID   0.00673459:0.00525008:0.699887:0.0564929:350471:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121778 ES:SE:LP:AF:SS:ID   0.00359063:0.00357505:0.501404:0.121778:350471:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121173 ES:SE:LP:AF:SS:ID   0.00212739:0.00381405:0.238826:0.121173:350471:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121007 ES:SE:LP:AF:SS:ID   0.00360082:0.00357696:0.50294:0.121007:350471:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00578592   ES:SE:LP:AF:SS:ID   0.000984935:0.0163667:0.0213571:0.00578592:350471:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.0018841    ES:SE:LP:AF:SS:ID   0.0170341:0.0308865:0.23561:0.0018841:350471:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869716 ES:SE:LP:AF:SS:ID   -0.00466826:0.00352663:0.731427:0.869716:350471:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00153228   ES:SE:LP:AF:SS:ID   0.000236453:0.0319033:0.00257604:0.00153228:350471:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.122935 ES:SE:LP:AF:SS:ID   0.00435763:0.00349246:0.673392:0.122935:350471:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142737 ES:SE:LP:AF:SS:ID   0.00379042:0.00345233:0.565056:0.142737:350471:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123052 ES:SE:LP:AF:SS:ID   0.00437973:0.00348758:0.679467:0.123052:350471:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.870852 ES:SE:LP:AF:SS:ID   -0.00472036:0.00339851:0.782914:0.870852:350471:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.875451 ES:SE:LP:AF:SS:ID   -0.00465198:0.00345487:0.749234:0.875451:350471:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128669 ES:SE:LP:AF:SS:ID   0.00455877:0.00340703:0.742607:0.128669:350471:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0363382    ES:SE:LP:AF:SS:ID   -0.00588594:0.00618568:0.466827:0.0363382:350471:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870445 ES:SE:LP:AF:SS:ID   -0.00479906:0.00339494:0.802769:0.870445:350471:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870547 ES:SE:LP:AF:SS:ID   -0.00479228:0.00339643:0.800651:0.870547:350471:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870441 ES:SE:LP:AF:SS:ID   -0.00484351:0.0033948:0.813453:0.870441:350471:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005043 ES:SE:LP:AF:SS:ID   0.0149545:0.0174395:0.407639:0.005043:350471:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00500987   ES:SE:LP:AF:SS:ID   0.0156005:0.017486:0.429105:0.00500987:350471:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00558061   ES:SE:LP:AF:SS:ID   0.0147708:0.0168914:0.418087:0.00558061:350471:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870525 ES:SE:LP:AF:SS:ID   -0.00505589:0.00338885:0.867353:0.870525:350471:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126013 ES:SE:LP:AF:SS:ID   0.00515693:0.00345814:0.866784:0.126013:350471:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.870088 ES:SE:LP:AF:SS:ID   -0.00494388:0.00338068:0.84274:0.870088:350471:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869366 ES:SE:LP:AF:SS:ID   -0.00487252:0.00337747:0.826467:0.869366:350471:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870242 ES:SE:LP:AF:SS:ID   -0.00498859:0.0033836:0.852664:0.870242:350471:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870248 ES:SE:LP:AF:SS:ID   -0.00498779:0.00338384:0.852379:0.870248:350471:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870256 ES:SE:LP:AF:SS:ID   -0.00498701:0.00338395:0.852148:0.870256:350471:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870699 ES:SE:LP:AF:SS:ID   -0.00497916:0.00339256:0.847116:0.870699:350471:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0988362    ES:SE:LP:AF:SS:ID   0.0057154:0.00394336:0.831992:0.0988362:350471:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00647162   ES:SE:LP:AF:SS:ID   0.0207355:0.0152603:0.758914:0.00647162:350471:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.874607 ES:SE:LP:AF:SS:ID   -0.00490712:0.00344321:0.812164:0.874607:350471:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863996 ES:SE:LP:AF:SS:ID   -0.00404582:0.00337656:0.636695:0.863996:350471:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.869379 ES:SE:LP:AF:SS:ID   -0.00477687:0.00340932:0.792694:0.869379:350471:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866338 ES:SE:LP:AF:SS:ID   -0.00464098:0.00341011:0.760623:0.866338:350471:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0987487    ES:SE:LP:AF:SS:ID   0.00553862:0.00408373:0.756922:0.0987487:350471:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.871521 ES:SE:LP:AF:SS:ID   -0.00483081:0.0034489:0.792339:0.871521:350471:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.871521 ES:SE:LP:AF:SS:ID   -0.00483042:0.0034489:0.792247:0.871521:350471:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.871521 ES:SE:LP:AF:SS:ID   -0.00481037:0.00344897:0.787549:0.871521:350471:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871892 ES:SE:LP:AF:SS:ID   -0.00470607:0.00345092:0.762811:0.871892:350471:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125594 ES:SE:LP:AF:SS:ID   0.00456108:0.00345366:0.729044:0.125594:350471:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105422 ES:SE:LP:AF:SS:ID   0.00478699:0.00376041:0.692461:0.105422:350471:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855337 ES:SE:LP:AF:SS:ID   -0.00409214:0.00337568:0.647006:0.855337:350471:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00164753   ES:SE:LP:AF:SS:ID   -0.007702:0.0299946:0.0983515:0.00164753:350471:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838288 ES:SE:LP:AF:SS:ID   0.00741875:0.00332509:1.59053:0.838288:350471:rs376645387