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-20546_3/ukb-d-20546_3.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-20546_3/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 16:39:52 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-20546_3/ukb-d-20546_3.vcf.gz ...
Read summary statistics for 13571577 SNPs.
Dropped 12694 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: 0.0545 (0.0049)
Lambda GC: 1.1197
Mean Chi^2: 1.1256
Intercept: 0.999 (0.0068)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 16:42:12 2019
Total time elapsed: 2.0m:19.62s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.0802,
    "mean_EFFECT": 0,
    "n": 117763,
    "n_snps": 13571577,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 1251354,
    "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": 564599,
    "n_est": 118187.1374,
    "ratio_se_n": 1.0018,
    "mean_diff": 0,
    "ratio_diff": 112.1074,
    "sd_y_est1": 0.4241,
    "sd_y_est2": 0.4249,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1283502,
    "ldsc_nsnp_merge_regression_ld": 1283502,
    "ldsc_observed_scale_h2_beta": 0.0545,
    "ldsc_observed_scale_h2_se": 0.0049,
    "ldsc_intercept_beta": 0.999,
    "ldsc_intercept_se": 0.0068,
    "ldsc_lambda_gc": 1.1197,
    "ldsc_mean_chisq": 1.1256,
    "ldsc_ratio": -0.008
}

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 13559547 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 57057 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33331 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.051448e+00 6.185082e+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 7.902510e+07 5.591576e+07 3.02000e+02 3.292824e+07 7.013902e+07 1.148584e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 2.430000e-05 1.023780e-02 -1.20752e-01 -2.810500e-03 3.400000e-06 2.821200e-03 1.188540e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 7.322500e-03 7.058400e-03 1.44710e-03 2.089900e-03 3.846200e-03 1.051410e-02 3.515570e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.874917e-01 2.920508e-01 1.00000e-07 2.315592e-01 4.832924e-01 7.403788e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.874904e-01 2.920516e-01 1.00000e-07 2.315567e-01 4.832903e-01 7.403788e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.909972e-01 2.544490e-01 1.00000e-03 7.483300e-03 6.026380e-02 2.956650e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 564599 0.9583984 NA NA NA NA NA 1.964726e-01 2.471287e-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 1.177630e+05 0.000000e+00 1.17763e+05 1.177630e+05 1.177630e+05 1.177630e+05 1.177630e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0011896 0.0030437 0.6959067 0.6959052 0.1116950 0.1894970 117763
1 693731 rs12238997 A G -0.0005539 0.0028824 0.8476050 0.8476050 0.1167150 0.1417730 117763
1 707522 rs371890604 G C -0.0005729 0.0032340 0.8593920 0.8593912 0.0983485 0.1293930 117763
1 717587 rs144155419 G A -0.0038319 0.0077498 0.6209920 0.6209910 0.0157583 0.0045926 117763
1 723329 rs189787166 A T 0.0029436 0.0225869 0.8963109 0.8963113 0.0017777 0.0003994 117763
1 730087 rs148120343 T C 0.0022322 0.0040112 0.5778738 0.5778732 0.0568966 0.0127796 117763
1 731718 rs142557973 T C 0.0005687 0.0027348 0.8352820 0.8352813 0.1226470 0.1543530 117763
1 732032 rs61770163 A C -0.0018983 0.0029149 0.5148966 0.5148960 0.1222350 0.1555510 117763
1 734349 rs141242758 T C 0.0003944 0.0027362 0.8854040 0.8854031 0.1219010 0.1525560 117763
1 740284 rs61770167 C T 0.0137681 0.0124875 0.2702253 0.2702224 0.0058608 0.0023962 117763
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0013652 0.0032155 0.6711645 0.6711632 0.0554823 0.0309934 117763
23 154923374 rs111332691 T A -0.0077439 0.0035062 0.0272007 0.0271990 0.0447339 0.0116556 117763
23 154925045 rs509981 C T 0.0016309 0.0016877 0.3338751 0.3338719 0.2447530 0.3634440 117763
23 154925895 rs538470 C T 0.0018114 0.0017258 0.2938982 0.2938960 0.2411730 0.3634440 117763
23 154927581 rs644138 G A 0.0011625 0.0015880 0.4641379 0.4641353 0.3005980 0.4635760 117763
23 154929412 rs557132 C T 0.0016097 0.0016881 0.3403157 0.3403138 0.2446280 0.3568210 117763
23 154929637 rs35185538 CT C 0.0024490 0.0017634 0.1649111 0.1649090 0.2288620 0.3011920 117763
23 154929952 rs4012982 CAA C 0.0018935 0.0017766 0.2865187 0.2865173 0.2387120 0.3165560 117763
23 154930230 rs781880 A G 0.0016444 0.0016876 0.3298679 0.3298658 0.2450460 0.3618540 117763
23 154930487 rs781879 T A 0.0094192 0.0057368 0.1006160 0.1006137 0.0196777 0.1263580 117763

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111695 ES:SE:LP:AF:SS:ID   0.00118965:0.00304372:0.157449:0.111695:117763:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116715 ES:SE:LP:AF:SS:ID   -0.000553922:0.00288238:0.0718065:0.116715:117763:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0983485    ES:SE:LP:AF:SS:ID   -0.000572897:0.00323398:0.0658087:0.0983485:117763:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157583    ES:SE:LP:AF:SS:ID   -0.00383188:0.00774984:0.206914:0.0157583:117763:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00177766   ES:SE:LP:AF:SS:ID   0.00294358:0.0225869:0.0475413:0.00177766:117763:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0568966    ES:SE:LP:AF:SS:ID   0.00223222:0.00401122:0.238167:0.0568966:117763:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122647 ES:SE:LP:AF:SS:ID   0.000568651:0.00273478:0.0781669:0.122647:117763:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122235 ES:SE:LP:AF:SS:ID   -0.00189826:0.00291487:0.28828:0.122235:117763:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121901 ES:SE:LP:AF:SS:ID   0.000394353:0.00273622:0.0528585:0.121901:117763:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00586084   ES:SE:LP:AF:SS:ID   0.0137681:0.0124875:0.568274:0.00586084:117763:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00183204   ES:SE:LP:AF:SS:ID   0.0414737:0.0239691:1.0779:0.00183204:117763:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869013 ES:SE:LP:AF:SS:ID   0.000364206:0.00269595:0.0493733:0.869013:117763:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00158489   ES:SE:LP:AF:SS:ID   0.0139828:0.0239673:0.252108:0.00158489:117763:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.123679 ES:SE:LP:AF:SS:ID   -0.000361517:0.00267132:0.0494653:0.123679:117763:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143308 ES:SE:LP:AF:SS:ID   0.000766765:0.00264201:0.112581:0.143308:117763:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123801 ES:SE:LP:AF:SS:ID   -0.000352354:0.00266763:0.0482172:0.123801:117763:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869786 ES:SE:LP:AF:SS:ID   -0.000887123:0.00259781:0.135052:0.869786:117763:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874576 ES:SE:LP:AF:SS:ID   -0.0003524:0.00264157:0.0487242:0.874576:117763:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129674 ES:SE:LP:AF:SS:ID   0.00050918:0.00260509:0.0731248:0.129674:117763:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0365183    ES:SE:LP:AF:SS:ID   0.00822937:0.00473105:1.0864:0.0365183:117763:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869381 ES:SE:LP:AF:SS:ID   -0.000936862:0.00259535:0.143804:0.869381:117763:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869453 ES:SE:LP:AF:SS:ID   -0.000952421:0.00259631:0.146459:0.869453:117763:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869381 ES:SE:LP:AF:SS:ID   -0.000920777:0.00259528:0.141013:0.869381:117763:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00512144   ES:SE:LP:AF:SS:ID   -0.00102035:0.0132701:0.0274681:0.00512144:117763:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00509053   ES:SE:LP:AF:SS:ID   -0.00139716:0.0132993:0.0379471:0.00509053:117763:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00568601   ES:SE:LP:AF:SS:ID   0.0013998:0.0127919:0.0395944:0.00568601:117763:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.869452 ES:SE:LP:AF:SS:ID   -0.00107732:0.00259048:0.169089:0.869452:117763:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126951 ES:SE:LP:AF:SS:ID   0.00044461:0.00264438:0.0622425:0.126951:117763:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.86897  ES:SE:LP:AF:SS:ID   -0.00102917:0.00258367:0.160911:0.86897:117763:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868378 ES:SE:LP:AF:SS:ID   -0.00105448:0.0025828:0.165531:0.868378:117763:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869162 ES:SE:LP:AF:SS:ID   -0.00108994:0.00258624:0.171704:0.869162:117763:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869169 ES:SE:LP:AF:SS:ID   -0.00109036:0.00258642:0.171767:0.869169:117763:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869171 ES:SE:LP:AF:SS:ID   -0.0010862:0.00258643:0.171008:0.869171:117763:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869607 ES:SE:LP:AF:SS:ID   -0.00115049:0.00259307:0.182252:0.869607:117763:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0995777    ES:SE:LP:AF:SS:ID   0.000369918:0.00301446:0.0446332:0.0995777:117763:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00654643   ES:SE:LP:AF:SS:ID   0.00725067:0.0116118:0.273801:0.00654643:117763:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873674 ES:SE:LP:AF:SS:ID   -0.000437766:0.00263294:0.0615058:0.873674:117763:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.862966 ES:SE:LP:AF:SS:ID   -0.00120151:0.0025821:0.192667:0.862966:117763:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868361 ES:SE:LP:AF:SS:ID   -0.000903095:0.00260693:0.137256:0.868361:117763:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865361 ES:SE:LP:AF:SS:ID   -0.000126105:0.00260727:0.017085:0.865361:117763:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0997465    ES:SE:LP:AF:SS:ID   0.000448078:0.00311571:0.0527384:0.0997465:117763:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870665 ES:SE:LP:AF:SS:ID   -5.9217e-05:0.00263817:0.00784783:0.870665:117763:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870665 ES:SE:LP:AF:SS:ID   -5.89825e-05:0.00263817:0.00781643:0.870665:117763:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870663 ES:SE:LP:AF:SS:ID   -6.30035e-05:0.00263818:0.00835446:0.870663:117763:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.870985 ES:SE:LP:AF:SS:ID   -0.000222197:0.00263929:0.0301616:0.870985:117763:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126537 ES:SE:LP:AF:SS:ID   0.00017586:0.00264155:0.0236863:0.126537:117763:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106315 ES:SE:LP:AF:SS:ID   0.00131084:0.00287073:0.188462:0.106315:117763:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854401 ES:SE:LP:AF:SS:ID   -0.00121103:0.00258147:0.194513:0.854401:117763:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00168927   ES:SE:LP:AF:SS:ID   0.00411654:0.0226875:0.0675166:0.00168927:117763:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839108 ES:SE:LP:AF:SS:ID   -0.00156871:0.00255743:0.267914:0.839108:117763:rs376645387