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-20548_2/ukb-d-20548_2.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-20548_2/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 16:37:50 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-20548_2/ukb-d-20548_2.vcf.gz ...
Read summary statistics for 13571112 SNPs.
Dropped 12693 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, 1283502 SNPs remain.
After merging with regression SNP LD, 1283502 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0332 (0.005)
Lambda GC: 1.0719
Mean Chi^2: 1.0745
Intercept: 0.9985 (0.0063)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 16:40:37 2019
Total time elapsed: 2.0m:46.72s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.0426,
    "mean_EFFECT": 0,
    "n": 114422,
    "n_snps": 13571112,
    "n_clumped_hits": 1,
    "n_p_sig": 1,
    "n_mono": 0,
    "n_ns": 1251339,
    "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": 564506,
    "n_est": 114833.6851,
    "ratio_se_n": 1.0018,
    "mean_diff": 0.0001,
    "ratio_diff": 37.576,
    "sd_y_est1": 0.3151,
    "sd_y_est2": 0.3157,
    "r2_sum1": 0,
    "r2_sum2": 0.0003,
    "r2_sum3": 0.0003,
    "r2_sum4": 0.0003,
    "ldsc_nsnp_merge_refpanel_ld": 1283502,
    "ldsc_nsnp_merge_regression_ld": 1283502,
    "ldsc_observed_scale_h2_beta": 0.0332,
    "ldsc_observed_scale_h2_se": 0.005,
    "ldsc_intercept_beta": 0.9985,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.0719,
    "ldsc_mean_chisq": 1.0745,
    "ldsc_ratio": -0.0201
}

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 13559083 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 57055 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33330 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.051395e+00 6.185088e+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 7.902494e+07 5.591569e+07 3.02000e+02 3.292811e+07 7.013911e+07 1.148580e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 2.370000e-05 7.689300e-03 -8.68090e-02 -2.086500e-03 -6.000000e-06 2.059600e-03 1.066530e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 5.518500e-03 5.319400e-03 1.08990e-03 1.575200e-03 2.898900e-03 7.923500e-03 2.647740e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.932797e-01 2.903759e-01 0.00000e+00 2.401840e-01 4.910119e-01 7.446119e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.932783e-01 2.903767e-01 0.00000e+00 2.401812e-01 4.910116e-01 7.446115e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.910036e-01 2.544513e-01 1.00000e-03 7.484800e-03 6.028400e-02 2.956750e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 564506 0.9584039 NA NA NA NA NA 1.964774e-01 2.471302e-01 0.00000e+00 5.790700e-03 8.446490e-02 3.083070e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 1.144220e+05 0.000000e+00 1.14422e+05 1.144220e+05 1.144220e+05 1.144220e+05 1.144220e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0005756 0.0022938 0.8018619 0.8018624 0.1117110 0.1894970 114422
1 693731 rs12238997 A G 0.0013787 0.0021732 0.5258017 0.5257996 0.1166780 0.1417730 114422
1 707522 rs371890604 G C 0.0014774 0.0024379 0.5444976 0.5444965 0.0983298 0.1293930 114422
1 717587 rs144155419 G A -0.0028077 0.0058412 0.6307525 0.6307515 0.0157526 0.0045926 114422
1 723329 rs189787166 A T 0.0250431 0.0169610 0.1398090 0.1398068 0.0017896 0.0003994 114422
1 730087 rs148120343 T C 0.0005585 0.0030233 0.8534500 0.8534496 0.0568713 0.0127796 114422
1 731718 rs142557973 T C 0.0014777 0.0020620 0.4736003 0.4736006 0.1226010 0.1543530 114422
1 732032 rs61770163 A C 0.0012491 0.0021975 0.5697627 0.5697622 0.1222220 0.1555510 114422
1 734349 rs141242758 T C 0.0014620 0.0020631 0.4785507 0.4785491 0.1218520 0.1525560 114422
1 740284 rs61770167 C T 0.0074418 0.0094083 0.4289543 0.4289527 0.0058609 0.0023962 114422
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0005484 0.0024244 0.8210290 0.8210285 0.0553623 0.0309934 114422
23 154923374 rs111332691 T A -0.0026716 0.0026390 0.3113709 0.3113673 0.0448384 0.0116556 114422
23 154925045 rs509981 C T 0.0013026 0.0012718 0.3057300 0.3057283 0.2446210 0.3634440 114422
23 154925895 rs538470 C T 0.0014705 0.0013006 0.2582290 0.2582246 0.2409890 0.3634440 114422
23 154927581 rs644138 G A 0.0010049 0.0011967 0.4010403 0.4010402 0.3003390 0.4635760 114422
23 154929412 rs557132 C T 0.0012851 0.0012721 0.3123863 0.3123863 0.2444980 0.3568210 114422
23 154929637 rs35185538 CT C 0.0016254 0.0013287 0.2212101 0.2212085 0.2287980 0.3011920 114422
23 154929952 rs4012982 CAA C 0.0013458 0.0013389 0.3148299 0.3148302 0.2385330 0.3165560 114422
23 154930230 rs781880 A G 0.0013373 0.0012717 0.2929962 0.2929944 0.2449060 0.3618540 114422
23 154930487 rs781879 T A -0.0006983 0.0043159 0.8714581 0.8714578 0.0197245 0.1263580 114422

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111711 ES:SE:LP:AF:SS:ID   0.000575588:0.00229375:0.0959004:0.111711:114422:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116678 ES:SE:LP:AF:SS:ID   0.00137872:0.00217316:0.279178:0.116678:114422:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0983298    ES:SE:LP:AF:SS:ID   0.00147741:0.00243786:0.264004:0.0983298:114422:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157526    ES:SE:LP:AF:SS:ID   -0.00280771:0.00584125:0.200141:0.0157526:114422:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00178963   ES:SE:LP:AF:SS:ID   0.0250431:0.016961:0.854465:0.00178963:114422:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0568713    ES:SE:LP:AF:SS:ID   0.000558453:0.00302326:0.0688219:0.0568713:114422:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122601 ES:SE:LP:AF:SS:ID   0.00147769:0.00206199:0.324588:0.122601:114422:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122222 ES:SE:LP:AF:SS:ID   0.00124908:0.00219753:0.244306:0.122222:114422:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121852 ES:SE:LP:AF:SS:ID   0.00146197:0.00206307:0.320072:0.121852:114422:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.0058609    ES:SE:LP:AF:SS:ID   0.00744184:0.00940832:0.367589:0.0058609:114422:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.0017873    ES:SE:LP:AF:SS:ID   0.00487272:0.0183862:0.101827:0.0017873:114422:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869045 ES:SE:LP:AF:SS:ID   -0.000498932:0.00203236:0.0936251:0.869045:114422:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00160053   ES:SE:LP:AF:SS:ID   0.00615346:0.0179763:0.135419:0.00160053:114422:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.123663 ES:SE:LP:AF:SS:ID   0.00070219:0.00201376:0.138276:0.123663:114422:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143266 ES:SE:LP:AF:SS:ID   0.00137732:0.00199179:0.310466:0.143266:114422:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123787 ES:SE:LP:AF:SS:ID   0.000735462:0.00201096:0.145955:0.123787:114422:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.86984  ES:SE:LP:AF:SS:ID   -0.000892877:0.00195879:0.188081:0.86984:114422:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874607 ES:SE:LP:AF:SS:ID   -0.000679043:0.00199141:0.134828:0.874607:114422:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129637 ES:SE:LP:AF:SS:ID   0.00113563:0.00196411:0.249385:0.129637:114422:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0366494    ES:SE:LP:AF:SS:ID   -0.0016844:0.00355999:0.196468:0.0366494:114422:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869441 ES:SE:LP:AF:SS:ID   -0.0010439:0.00195693:0.226409:0.869441:114422:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869515 ES:SE:LP:AF:SS:ID   -0.000967567:0.00195767:0.206814:0.869515:114422:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.86944  ES:SE:LP:AF:SS:ID   -0.00103623:0.00195688:0.224435:0.86944:114422:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00515222   ES:SE:LP:AF:SS:ID   -0.00849978:0.00996431:0.404891:0.00515222:114422:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00512096   ES:SE:LP:AF:SS:ID   -0.00836568:0.00998628:0.395568:0.00512096:114422:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00569216   ES:SE:LP:AF:SS:ID   0.0159219:0.00963909:1.00623:0.00569216:114422:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.869508 ES:SE:LP:AF:SS:ID   -0.000818878:0.0019533:0.170664:0.869508:114422:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126917 ES:SE:LP:AF:SS:ID   0.000652338:0.00199366:0.128712:0.126917:114422:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.869029 ES:SE:LP:AF:SS:ID   -0.000625393:0.00194816:0.125983:0.869029:114422:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868436 ES:SE:LP:AF:SS:ID   -0.000939199:0.00194751:0.200918:0.868436:114422:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869215 ES:SE:LP:AF:SS:ID   -0.000717104:0.00195009:0.146864:0.869215:114422:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869222 ES:SE:LP:AF:SS:ID   -0.000717437:0.00195023:0.146929:0.869222:114422:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869224 ES:SE:LP:AF:SS:ID   -0.000716653:0.00195024:0.146746:0.869224:114422:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869663 ES:SE:LP:AF:SS:ID   -0.000871571:0.00195526:0.183246:0.869663:114422:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.099489 ES:SE:LP:AF:SS:ID   0.00128714:0.00227301:0.243203:0.099489:114422:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00655187   ES:SE:LP:AF:SS:ID   0.00967036:0.00874813:0.57028:0.00655187:114422:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873708 ES:SE:LP:AF:SS:ID   -0.000479719:0.00198494:0.0920359:0.873708:114422:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863017 ES:SE:LP:AF:SS:ID   -0.000917058:0.0019471:0.195417:0.863017:114422:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868401 ES:SE:LP:AF:SS:ID   -0.000467326:0.00196555:0.0904071:0.868401:114422:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865415 ES:SE:LP:AF:SS:ID   1.42937e-05:0.00196576:0.00252711:0.865415:114422:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0997473    ES:SE:LP:AF:SS:ID   0.00126261:0.00234867:0.228512:0.0997473:114422:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870695 ES:SE:LP:AF:SS:ID   -0.000287268:0.00198886:0.0529807:0.870695:114422:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870695 ES:SE:LP:AF:SS:ID   -0.000287392:0.00198886:0.0530052:0.870695:114422:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870693 ES:SE:LP:AF:SS:ID   -0.000289931:0.00198886:0.0535001:0.870693:114422:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871018 ES:SE:LP:AF:SS:ID   -0.000311887:0.00198974:0.0577716:0.871018:114422:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126512 ES:SE:LP:AF:SS:ID   0.000346544:0.00199141:0.0645678:0.126512:114422:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106279 ES:SE:LP:AF:SS:ID   0.00203742:0.00216399:0.460366:0.106279:114422:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854464 ES:SE:LP:AF:SS:ID   -0.00116848:0.0019465:0.260976:0.854464:114422:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00170509   ES:SE:LP:AF:SS:ID   0.0233469:0.0170125:0.769651:0.00170509:114422:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839076 ES:SE:LP:AF:SS:ID   -0.00258515:0.00192713:0.745268:0.839076:114422:rs376645387