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-5699/ukb-d-5699.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-5699/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 17:18:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-5699/ukb-d-5699.vcf.gz ...
Read summary statistics for 13520622 SNPs.
Dropped 12615 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, 1283494 SNPs remain.
After merging with regression SNP LD, 1283494 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1504 (0.024)
Lambda GC: 1.0684
Mean Chi^2: 1.0617
Intercept: 0.9997 (0.0063)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 17:21:02 2019
Total time elapsed: 2.0m:43.19s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.952,
    "inflation_factor": 1.0412,
    "mean_EFFECT": 6.3394e-06,
    "n": 20880,
    "n_snps": 13520622,
    "n_clumped_hits": 0,
    "n_p_sig": 1,
    "n_mono": 0,
    "n_ns": 1249538,
    "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": 551395,
    "n_est": 21012.9908,
    "ratio_se_n": 1.0032,
    "mean_diff": 7.822e-06,
    "ratio_diff": 4.423,
    "sd_y_est1": 0.3967,
    "sd_y_est2": 0.398,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1283494,
    "ldsc_nsnp_merge_regression_ld": 1283494,
    "ldsc_observed_scale_h2_beta": 0.1504,
    "ldsc_observed_scale_h2_se": 0.024,
    "ldsc_intercept_beta": 0.9997,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.0684,
    "ldsc_mean_chisq": 1.0617,
    "ldsc_ratio": -0.0049
}

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 13508670 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 56973 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33302 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.052088e+00 6.184878e+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 7.901959e+07 5.591976e+07 3.02000e+02 3.292036e+07 7.012266e+07 1.148509e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 6.300000e-06 2.241590e-02 -3.23187e-01 -5.983500e-03 3.590000e-05 6.158800e-03 2.474570e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.610190e-02 1.549190e-02 3.12690e-03 4.636700e-03 8.485200e-03 2.307150e-02 8.420230e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.936639e-01 2.900940e-01 0.00000e+00 2.409301e-01 4.913025e-01 7.449069e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.936562e-01 2.900986e-01 0.00000e+00 2.409168e-01 4.912944e-01 7.449043e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.916849e-01 2.546239e-01 1.00000e-03 7.667900e-03 6.100940e-02 2.971050e-01 9.989990e-01 ▇▂▁▁▁
numeric AF_reference 551395 0.9592182 NA NA NA NA NA 1.969937e-01 2.472505e-01 0.00000e+00 5.790700e-03 8.526360e-02 3.093050e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 2.088000e+04 0.000000e+00 2.08800e+04 2.088000e+04 2.088000e+04 2.088000e+04 2.088000e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0046670 0.0068787 0.4974827 0.4974748 0.1092550 0.1894970 20880
1 693731 rs12238997 A G -0.0080712 0.0065300 0.2164691 0.2164551 0.1146360 0.1417730 20880
1 707522 rs371890604 G C -0.0087256 0.0073136 0.2328558 0.2328421 0.0965432 0.1293930 20880
1 717587 rs144155419 G A -0.0008191 0.0178298 0.9633580 0.9633580 0.0149597 0.0045926 20880
1 723329 rs189787166 A T 0.0367867 0.0474654 0.4383359 0.4383270 0.0019623 0.0003994 20880
1 730087 rs148120343 T C 0.0006674 0.0091746 0.9420080 0.9420073 0.0544090 0.0127796 20880
1 731718 rs142557973 T C -0.0089985 0.0062040 0.1469528 0.1469377 0.1201900 0.1543530 20880
1 732032 rs61770163 A C -0.0085773 0.0065974 0.1935811 0.1935661 0.1198670 0.1555510 20880
1 734349 rs141242758 T C -0.0093157 0.0062105 0.1336359 0.1336209 0.1193220 0.1525560 20880
1 740284 rs61770167 C T -0.0605304 0.0274132 0.0272496 0.0272390 0.0060259 0.0023962 20880
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0007704 0.0069405 0.9116140 0.9116126 0.0557888 0.0309934 20880
23 154923374 rs111332691 T A -0.0107571 0.0076539 0.1599039 0.1598882 0.0447797 0.0116556 20880
23 154925045 rs509981 C T -0.0019601 0.0036491 0.5911749 0.5911693 0.2447100 0.3634440 20880
23 154925895 rs538470 C T -0.0017656 0.0037335 0.6362928 0.6362876 0.2411560 0.3634440 20880
23 154927581 rs644138 G A -0.0020893 0.0034366 0.5432328 0.5432263 0.3008730 0.4635760 20880
23 154929412 rs557132 C T -0.0019826 0.0036502 0.5870377 0.5870313 0.2445660 0.3568210 20880
23 154929637 rs35185538 CT C -0.0023277 0.0038157 0.5418449 0.5418397 0.2282850 0.3011920 20880
23 154929952 rs4012982 CAA C -0.0010389 0.0038346 0.7864425 0.7864403 0.2392740 0.3165560 20880
23 154930230 rs781880 A G -0.0019559 0.0036498 0.5920386 0.5920312 0.2451460 0.3618540 20880
23 154930487 rs781879 T A -0.0039175 0.0126582 0.7569584 0.7569546 0.0193643 0.1263580 20880

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.109255 ES:SE:LP:AF:SS:ID   -0.00466696:0.00687867:0.303222:0.109255:20880:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.114636 ES:SE:LP:AF:SS:ID   -0.00807119:0.00653004:0.664604:0.114636:20880:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0965432    ES:SE:LP:AF:SS:ID   -0.00872561:0.00731358:0.632913:0.0965432:20880:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0149597    ES:SE:LP:AF:SS:ID   -0.000819103:0.0178298:0.0162123:0.0149597:20880:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00196229   ES:SE:LP:AF:SS:ID   0.0367867:0.0474654:0.358193:0.00196229:20880:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.054409 ES:SE:LP:AF:SS:ID   0.00066743:0.00917465:0.0259454:0.054409:20880:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12019  ES:SE:LP:AF:SS:ID   -0.00899846:0.00620398:0.832822:0.12019:20880:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.119867 ES:SE:LP:AF:SS:ID   -0.00857733:0.00659743:0.713137:0.119867:20880:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.119322 ES:SE:LP:AF:SS:ID   -0.00931567:0.00621055:0.874077:0.119322:20880:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00602594   ES:SE:LP:AF:SS:ID   -0.0605304:0.0274132:1.56464:0.00602594:20880:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.0017217    ES:SE:LP:AF:SS:ID   0.0643231:0.0559088:0.602145:0.0017217:20880:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.871166 ES:SE:LP:AF:SS:ID   0.00782829:0.00610208:0.699959:0.871166:20880:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00155135   ES:SE:LP:AF:SS:ID   -0.0187339:0.0536944:0.138365:0.00155135:20880:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.121655 ES:SE:LP:AF:SS:ID   -0.00683653:0.00604029:0.588847:0.121655:20880:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.141198 ES:SE:LP:AF:SS:ID   -0.00435758:0.00596561:0.332431:0.141198:20880:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.121783 ES:SE:LP:AF:SS:ID   -0.00666109:0.00603257:0.569403:0.121783:20880:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.872029 ES:SE:LP:AF:SS:ID   0.00569328:0.00587242:0.478457:0.872029:20880:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.8767   ES:SE:LP:AF:SS:ID   0.00484029:0.00596746:0.379541:0.8767:20880:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.12773  ES:SE:LP:AF:SS:ID   -0.00605964:0.00588477:0.518334:0.12773:20880:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0362308    ES:SE:LP:AF:SS:ID   0.0116254:0.0105501:0.567817:0.0362308:20880:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.871537 ES:SE:LP:AF:SS:ID   0.00553452:0.0058634:0.461897:0.871537:20880:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.871664 ES:SE:LP:AF:SS:ID   0.00500138:0.00586801:0.404448:0.871664:20880:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.871535 ES:SE:LP:AF:SS:ID   0.0055217:0.00586302:0.460531:0.871535:20880:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00493783   ES:SE:LP:AF:SS:ID   0.0367377:0.0297983:0.662273:0.00493783:20880:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00490947   ES:SE:LP:AF:SS:ID   0.0376406:0.0298573:0.683114:0.00490947:20880:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00488215   ES:SE:LP:AF:SS:ID   0.0202435:0.0307514:0.292127:0.00488215:20880:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.871701 ES:SE:LP:AF:SS:ID   0.00561462:0.00585701:0.471387:0.871701:20880:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.124895 ES:SE:LP:AF:SS:ID   -0.00594584:0.00597916:0.494815:0.124895:20880:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.871216 ES:SE:LP:AF:SS:ID   0.00543926:0.00584126:0.453739:0.871216:20880:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.870519 ES:SE:LP:AF:SS:ID   0.00541652:0.00583665:0.451722:0.870519:20880:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.87144  ES:SE:LP:AF:SS:ID   0.0056452:0.00584836:0.475703:0.87144:20880:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.871446 ES:SE:LP:AF:SS:ID   0.00564638:0.00584881:0.475784:0.871446:20880:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.871455 ES:SE:LP:AF:SS:ID   0.00558436:0.00584901:0.468887:0.871455:20880:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.871866 ES:SE:LP:AF:SS:ID   0.00557282:0.00586387:0.466053:0.871866:20880:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.098151 ES:SE:LP:AF:SS:ID   -0.0100361:0.00680244:0.853469:0.098151:20880:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00567444   ES:SE:LP:AF:SS:ID   0.0485325:0.0279708:1.0823:0.00567444:20880:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.875856 ES:SE:LP:AF:SS:ID   0.00519608:0.00595222:0.417149:0.875856:20880:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.865217 ES:SE:LP:AF:SS:ID   0.00566199:0.00583888:0.478594:0.865217:20880:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.8709   ES:SE:LP:AF:SS:ID   0.00572311:0.00589608:0.479219:0.8709:20880:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.867505 ES:SE:LP:AF:SS:ID   0.00593418:0.00589918:0.502435:0.867505:20880:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0977569    ES:SE:LP:AF:SS:ID   -0.00608399:0.00706583:0.409802:0.0977569:20880:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.872776 ES:SE:LP:AF:SS:ID   0.00552813:0.00596102:0.451316:0.872776:20880:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.872776 ES:SE:LP:AF:SS:ID   0.00552813:0.00596102:0.451316:0.872776:20880:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.872797 ES:SE:LP:AF:SS:ID   0.00556042:0.00596266:0.454608:0.872797:20880:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.873104 ES:SE:LP:AF:SS:ID   0.0053762:0.00596529:0.434782:0.873104:20880:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.12453  ES:SE:LP:AF:SS:ID   -0.00620946:0.00597148:0.525172:0.12453:20880:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.104836 ES:SE:LP:AF:SS:ID   -0.00694421:0.00647724:0.547156:0.104836:20880:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855954 ES:SE:LP:AF:SS:ID   0.00717847:0.00583817:0.659814:0.855954:20880:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00183514   ES:SE:LP:AF:SS:ID   0.0416673:0.0479102:0.41513:0.00183514:20880:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838756 ES:SE:LP:AF:SS:ID   -0.00866447:0.00566467:0.899147:0.838756:20880:rs376645387