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-30070_irnt/ukb-d-30070_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-30070_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 14:58:53 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-30070_irnt/ukb-d-30070_irnt.vcf.gz ...
Read summary statistics for 13586287 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: 0.1858 (0.0152)
Lambda GC: 1.4587
Mean Chi^2: 2.4843
Intercept: 1.1289 (0.0192)
Ratio: 0.0868 (0.0129)
Analysis finished at Mon Nov 25 15:01:38 2019
Total time elapsed: 2.0m:44.21s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.2971,
    "mean_EFFECT": 0.0001,
    "n": 350473,
    "n_snps": 13586287,
    "n_clumped_hits": 310,
    "n_p_sig": 95344,
    "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": 351808.1526,
    "ratio_se_n": 1.0019,
    "mean_diff": -9.0019e-07,
    "ratio_diff": 0.8983,
    "sd_y_est1": 0.9966,
    "sd_y_est2": 0.9985,
    "r2_sum1": 0.118,
    "r2_sum2": 0.1187,
    "r2_sum3": 0.1183,
    "r2_sum4": 0.1185,
    "ldsc_nsnp_merge_refpanel_ld": 1283504,
    "ldsc_nsnp_merge_regression_ld": 1283504,
    "ldsc_observed_scale_h2_beta": 0.1858,
    "ldsc_observed_scale_h2_se": 0.0152,
    "ldsc_intercept_beta": 1.1289,
    "ldsc_intercept_se": 0.0192,
    "ldsc_lambda_gc": 1.4587,
    "ldsc_mean_chisq": 2.4843,
    "ldsc_ratio": 0.0868
}

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 13574241 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 5.591482e+07 3.02000e+02 3.293109e+07 7.013885e+07 1.148590e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 6.970000e-05 1.492800e-02 -3.45285e-01 -4.367400e-03 2.070000e-05 4.417600e-03 3.176390e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.000350e-02 9.649700e-03 1.95660e-03 2.848000e-03 5.248200e-03 1.437600e-02 4.892070e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.555702e-01 3.023290e-01 0.00000e+00 1.828332e-01 4.423815e-01 7.174820e-01 1.000000e+00 ▇▆▆▆▅
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.555697e-01 3.023293e-01 0.00000e+00 1.828325e-01 4.423817e-01 7.174818e-01 9.999999e-01 ▇▆▆▆▅
numeric AF 0 1.0000000 NA NA NA NA NA 1.908060e-01 2.544034e-01 1.00000e-03 7.430500e-03 6.003390e-02 2.953170e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 569192 0.9581054 NA NA NA NA NA 1.963356e-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 3.504730e+05 0.000000e+00 3.50473e+05 3.504730e+05 3.504730e+05 3.504730e+05 3.504730e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0025104 0.0041675 0.5469190 0.5469188 0.1106770 0.1894970 350473
1 693731 rs12238997 A G 0.0024944 0.0039378 0.5264389 0.5264388 0.1158570 0.1417730 350473
1 707522 rs371890604 G C 0.0017955 0.0044283 0.6851348 0.6851328 0.0972842 0.1293930 350473
1 717587 rs144155419 G A 0.0047094 0.0105546 0.6554582 0.6554571 0.0157161 0.0045926 350473
1 723329 rs189787166 A T -0.0626076 0.0312778 0.0453221 0.0453210 0.0017252 0.0003994 350473
1 730087 rs148120343 T C 0.0030858 0.0054842 0.5736606 0.5736604 0.0564940 0.0127796 350473
1 731718 rs142557973 T C 0.0025824 0.0037344 0.4892547 0.4892551 0.1217800 0.1543530 350473
1 732032 rs61770163 A C 0.0034922 0.0039841 0.3807439 0.3807438 0.1211760 0.1555510 350473
1 734349 rs141242758 T C 0.0028983 0.0037364 0.4379323 0.4379316 0.1210090 0.1525560 350473
1 740284 rs61770167 C T -0.0168069 0.0170947 0.3255270 0.3255265 0.0057871 0.0023962 350473
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0007677 0.0043136 0.8587410 0.8587413 0.0561534 0.0309934 350473
23 154923374 rs111332691 T A -0.0025399 0.0047418 0.5922117 0.5922107 0.0447909 0.0116556 350473
23 154925045 rs509981 C T 0.0012202 0.0022819 0.5928475 0.5928456 0.2454630 0.3634440 350473
23 154925895 rs538470 C T 0.0014867 0.0023341 0.5241445 0.5241433 0.2418100 0.3634440 350473
23 154927581 rs644138 G A 0.0008776 0.0021457 0.6825288 0.6825282 0.3020120 0.4635760 350473
23 154929412 rs557132 C T 0.0012942 0.0022826 0.5707186 0.5707189 0.2453180 0.3568210 350473
23 154929637 rs35185538 CT C 0.0006904 0.0023825 0.7719941 0.7719954 0.2295400 0.3011920 350473
23 154929952 rs4012982 CAA C 0.0012334 0.0024007 0.6074241 0.6074239 0.2392730 0.3165560 350473
23 154930230 rs781880 A G 0.0014681 0.0022822 0.5200283 0.5200276 0.2457280 0.3618540 350473
23 154930487 rs781879 T A 0.0082416 0.0078317 0.2926483 0.2926478 0.0195299 0.1263580 350473

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.110677 ES:SE:LP:AF:SS:ID   0.00251041:0.00416746:0.262077:0.110677:350473:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.115857 ES:SE:LP:AF:SS:ID   0.00249438:0.00393776:0.278652:0.115857:350473:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0972842    ES:SE:LP:AF:SS:ID   0.00179553:0.00442828:0.164224:0.0972842:350473:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157161    ES:SE:LP:AF:SS:ID   0.0047094:0.0105546:0.183455:0.0157161:350473:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00172519   ES:SE:LP:AF:SS:ID   -0.0626076:0.0312778:1.34369:0.00172519:350473:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.056494 ES:SE:LP:AF:SS:ID   0.00308576:0.00548415:0.241345:0.056494:350473:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12178  ES:SE:LP:AF:SS:ID   0.00258235:0.00373445:0.310465:0.12178:350473:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121176 ES:SE:LP:AF:SS:ID   0.00349217:0.0039841:0.419367:0.121176:350473:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121009 ES:SE:LP:AF:SS:ID   0.00289833:0.00373645:0.358593:0.121009:350473:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00578713   ES:SE:LP:AF:SS:ID   -0.0168069:0.0170947:0.487413:0.00578713:350473:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00188409   ES:SE:LP:AF:SS:ID   0.00833918:0.0322638:0.0990618:0.00188409:350473:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869714 ES:SE:LP:AF:SS:ID   -0.00450519:0.00368387:0.654922:0.869714:350473:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00153228   ES:SE:LP:AF:SS:ID   0.0139299:0.033326:0.170082:0.00153228:350473:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.122937 ES:SE:LP:AF:SS:ID   0.00558174:0.00364817:0.899578:0.122937:350473:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142739 ES:SE:LP:AF:SS:ID   0.00469544:0.00360626:0.71465:0.142739:350473:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123054 ES:SE:LP:AF:SS:ID   0.00553822:0.00364307:0.891232:0.123054:350473:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.87085  ES:SE:LP:AF:SS:ID   -0.00522001:0.00355004:0.849394:0.87085:350473:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.875449 ES:SE:LP:AF:SS:ID   -0.00560305:0.00360891:0.918908:0.875449:350473:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128671 ES:SE:LP:AF:SS:ID   0.00497126:0.00355893:0.789248:0.128671:350473:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0363366    ES:SE:LP:AF:SS:ID   0.00339196:0.00646164:0.222119:0.0363366:350473:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870443 ES:SE:LP:AF:SS:ID   -0.0049124:0.00354631:0.779926:0.870443:350473:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870545 ES:SE:LP:AF:SS:ID   -0.00467838:0.00354787:0.727488:0.870545:350473:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870438 ES:SE:LP:AF:SS:ID   -0.00491114:0.00354616:0.779685:0.870438:350473:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00504296   ES:SE:LP:AF:SS:ID   0.0323437:0.0182172:1.12019:0.00504296:350473:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00500985   ES:SE:LP:AF:SS:ID   0.0327457:0.0182657:1.13658:0.00500985:350473:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00558058   ES:SE:LP:AF:SS:ID   0.00148931:0.0176446:0.0302422:0.00558058:350473:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870523 ES:SE:LP:AF:SS:ID   -0.00513332:0.00353995:0.8326:0.870523:350473:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126015 ES:SE:LP:AF:SS:ID   0.00503801:0.00361233:0.787501:0.126015:350473:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.870086 ES:SE:LP:AF:SS:ID   -0.00511952:0.00353141:0.832269:0.870086:350473:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869364 ES:SE:LP:AF:SS:ID   -0.00473634:0.00352806:0.746073:0.869364:350473:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.87024  ES:SE:LP:AF:SS:ID   -0.00502182:0.00353446:0.808627:0.87024:350473:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870246 ES:SE:LP:AF:SS:ID   -0.00501845:0.00353472:0.80777:0.870246:350473:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870254 ES:SE:LP:AF:SS:ID   -0.00502992:0.00353483:0.810372:0.870254:350473:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870697 ES:SE:LP:AF:SS:ID   -0.00524854:0.00354382:0.858243:0.870697:350473:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0988385    ES:SE:LP:AF:SS:ID   0.0036799:0.00411917:0.429847:0.0988385:350473:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00647157   ES:SE:LP:AF:SS:ID   0.0158327:0.0159409:0.494028:0.00647157:350473:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.874605 ES:SE:LP:AF:SS:ID   -0.00471784:0.00359673:0.722111:0.874605:350473:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863994 ES:SE:LP:AF:SS:ID   -0.00477142:0.00352711:0.754179:0.863994:350473:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.869377 ES:SE:LP:AF:SS:ID   -0.00515182:0.00356133:0.829709:0.869377:350473:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866336 ES:SE:LP:AF:SS:ID   -0.00475968:0.00356216:0.741143:0.866336:350473:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0987495    ES:SE:LP:AF:SS:ID   0.00642844:0.00426582:0.880015:0.0987495:350473:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.871519 ES:SE:LP:AF:SS:ID   -0.00515974:0.00360267:0.817905:0.871519:350473:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.871519 ES:SE:LP:AF:SS:ID   -0.00515983:0.00360267:0.817925:0.871519:350473:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.871519 ES:SE:LP:AF:SS:ID   -0.00514691:0.00360274:0.814974:0.871519:350473:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.87189  ES:SE:LP:AF:SS:ID   -0.0055329:0.00360478:0.903733:0.87189:350473:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125596 ES:SE:LP:AF:SS:ID   0.00460841:0.00360765:0.695807:0.125596:350473:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105424 ES:SE:LP:AF:SS:ID   0.00265747:0.00392807:0.302159:0.105424:350473:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855335 ES:SE:LP:AF:SS:ID   -0.00459126:0.00352619:0.714666:0.855335:350473:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00164752   ES:SE:LP:AF:SS:ID   -0.0675526:0.031332:1.50749:0.00164752:350473:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838287 ES:SE:LP:AF:SS:ID   0.00140024:0.00347338:0.163138:0.838287:350473:rs376645387