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-22650_9/ukb-d-22650_9.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-22650_9/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 16:39:41 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22650_9/ukb-d-22650_9.vcf.gz ...
Read summary statistics for 13531002 SNPs.
Dropped 12637 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: 0.0389 (0.0201)
Lambda GC: 1.0177
Mean Chi^2: 1.0183
Intercept: 0.9984 (0.0069)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 16:41:43 2019
Total time elapsed: 2.0m:2.05s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.952,
    "inflation_factor": 1.0143,
    "mean_EFFECT": -3.555e-06,
    "n": 25697,
    "n_snps": 13531002,
    "n_clumped_hits": 1,
    "n_p_sig": 60,
    "n_mono": 0,
    "n_ns": 1249835,
    "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": 555297,
    "n_est": 25876.1934,
    "ratio_se_n": 1.0035,
    "mean_diff": -0,
    "ratio_diff": 4.8175,
    "sd_y_est1": 0.186,
    "sd_y_est2": 0.1867,
    "r2_sum1": 0,
    "r2_sum2": 0.0013,
    "r2_sum3": 0.0012,
    "r2_sum4": 0.0012,
    "ldsc_nsnp_merge_refpanel_ld": 1283494,
    "ldsc_nsnp_merge_regression_ld": 1283494,
    "ldsc_observed_scale_h2_beta": 0.0389,
    "ldsc_observed_scale_h2_se": 0.0201,
    "ldsc_intercept_beta": 0.9984,
    "ldsc_intercept_se": 0.0069,
    "ldsc_lambda_gc": 1.0177,
    "ldsc_mean_chisq": 1.0183,
    "ldsc_ratio": -0.0874
}

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 13519028 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 56985 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33309 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.051469e+00 6.184688e+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 7.902182e+07 5.591791e+07 3.02000e+02 3.292010e+07 7.013031e+07 1.148571e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -3.600000e-06 9.470900e-03 -1.73255e-01 -2.397200e-03 8.990000e-05 2.649700e-03 1.018340e-01 ▁▁▁▇▁
numeric SE 0 1.0000000 NA NA NA NA NA 6.823300e-03 6.570200e-03 1.31390e-03 1.960800e-03 3.592900e-03 9.774600e-03 3.807380e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.984423e-01 2.881773e-01 0.00000e+00 2.494061e-01 4.969595e-01 7.476615e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.984361e-01 2.881811e-01 0.00000e+00 2.493952e-01 4.969537e-01 7.476595e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.915411e-01 2.545891e-01 1.00000e-03 7.623900e-03 6.088630e-02 2.968220e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 555297 0.9589611 NA NA NA NA NA 1.968893e-01 2.472270e-01 0.00000e+00 5.790700e-03 8.506390e-02 3.091390e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 2.569700e+04 0.000000e+00 2.56970e+04 2.569700e+04 2.569700e+04 2.569700e+04 2.569700e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0003401 0.0028544 0.9051630 0.9051624 0.1121430 0.1894970 25697
1 693731 rs12238997 A G 0.0007308 0.0027259 0.7886186 0.7886168 0.1165660 0.1417730 25697
1 707522 rs371890604 G C 0.0003050 0.0030687 0.9208360 0.9208356 0.0975760 0.1293930 25697
1 717587 rs144155419 G A -0.0033155 0.0072323 0.6466436 0.6466395 0.0160183 0.0045926 25697
1 723329 rs189787166 A T 0.0044594 0.0200407 0.8239119 0.8239109 0.0019672 0.0003994 25697
1 730087 rs148120343 T C -0.0018289 0.0037440 0.6252015 0.6251975 0.0579332 0.0127796 25697
1 731718 rs142557973 T C 0.0003901 0.0025834 0.8799770 0.8799763 0.1229090 0.1543530 25697
1 732032 rs61770163 A C 0.0007232 0.0027523 0.7927459 0.7927434 0.1227700 0.1555510 25697
1 734349 rs141242758 T C 0.0003597 0.0025849 0.8893340 0.8893326 0.1221900 0.1525560 25697
1 740284 rs61770167 C T -0.0088073 0.0122492 0.4721413 0.4721336 0.0054168 0.0023962 25697
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0024954 0.0028827 0.3866936 0.3866867 0.0569276 0.0309934 25697
23 154923374 rs111332691 T A 0.0018559 0.0031930 0.5610854 0.5610805 0.0445188 0.0116556 25697
23 154925045 rs509981 C T -0.0002231 0.0015476 0.8853620 0.8853602 0.2412010 0.3634440 25697
23 154925895 rs538470 C T 0.0002065 0.0015816 0.8961359 0.8961349 0.2377450 0.3634440 25697
23 154927581 rs644138 G A 0.0004942 0.0014500 0.7332685 0.7332646 0.2986200 0.4635760 25697
23 154929412 rs557132 C T -0.0002509 0.0015483 0.8712600 0.8712586 0.2409870 0.3568210 25697
23 154929637 rs35185538 CT C -0.0000987 0.0016133 0.9512091 0.9512083 0.2268160 0.3011920 25697
23 154929952 rs4012982 CAA C -0.0001729 0.0016299 0.9155050 0.9155041 0.2348550 0.3165560 25697
23 154930230 rs781880 A G -0.0001069 0.0015473 0.9448960 0.9448955 0.2415200 0.3618540 25697
23 154930487 rs781879 T A 0.0055642 0.0053693 0.3000751 0.3000653 0.0189835 0.1263580 25697

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.112143 ES:SE:LP:AF:SS:ID   -0.000340084:0.00285443:0.0432732:0.112143:25697:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116566 ES:SE:LP:AF:SS:ID   0.000730836:0.00272591:0.103133:0.116566:25697:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.097576 ES:SE:LP:AF:SS:ID   0.000304973:0.00306872:0.0358177:0.097576:25697:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0160183    ES:SE:LP:AF:SS:ID   -0.00331553:0.00723227:0.189335:0.0160183:25697:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00196719   ES:SE:LP:AF:SS:ID   0.00445941:0.0200407:0.0841192:0.00196719:25697:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0579332    ES:SE:LP:AF:SS:ID   -0.00182891:0.00374395:0.20398:0.0579332:25697:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122909 ES:SE:LP:AF:SS:ID   0.000390087:0.00258337:0.0555287:0.122909:25697:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.12277  ES:SE:LP:AF:SS:ID   0.000723161:0.00275228:0.100866:0.12277:25697:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.12219  ES:SE:LP:AF:SS:ID   0.000359683:0.00258488:0.0509351:0.12219:25697:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00541682   ES:SE:LP:AF:SS:ID   -0.00880732:0.0122492:0.325928:0.00541682:25697:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00179627   ES:SE:LP:AF:SS:ID   0.0111447:0.022873:0.203363:0.00179627:25697:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.867932 ES:SE:LP:AF:SS:ID   -0.000222261:0.0025374:0.0314237:0.867932:25697:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.0017537    ES:SE:LP:AF:SS:ID   -0.00390531:0.0212265:0.0685279:0.0017537:25697:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.12446  ES:SE:LP:AF:SS:ID   -0.000375046:0.00251698:0.0547531:0.12446:25697:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143673 ES:SE:LP:AF:SS:ID   -0.00129797:0.00249528:0.21972:0.143673:25697:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.124586 ES:SE:LP:AF:SS:ID   -0.000395281:0.00251381:0.0579651:0.124586:25697:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869298 ES:SE:LP:AF:SS:ID   9.82812e-05:0.00245244:0.0141094:0.869298:25697:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.873888 ES:SE:LP:AF:SS:ID   0.000358736:0.00249043:0.0528286:0.873888:25697:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.130223 ES:SE:LP:AF:SS:ID   -0.000164009:0.00245998:0.0237216:0.130223:25697:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0356073    ES:SE:LP:AF:SS:ID   0.00744195:0.00449796:1.00862:0.0356073:25697:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86893  ES:SE:LP:AF:SS:ID   0.000101243:0.00245066:0.0145524:0.86893:25697:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869072 ES:SE:LP:AF:SS:ID   0.000140388:0.00245206:0.0202951:0.869072:25697:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.868931 ES:SE:LP:AF:SS:ID   0.000100998:0.00245057:0.0145169:0.868931:25697:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.0049226    ES:SE:LP:AF:SS:ID   -0.00751678:0.0128383:0.253196:0.0049226:25697:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00489216   ES:SE:LP:AF:SS:ID   -0.00758848:0.0128685:0.255393:0.00489216:25697:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00561735   ES:SE:LP:AF:SS:ID   0.0125272:0.0120208:0.526713:0.00561735:25697:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.868988 ES:SE:LP:AF:SS:ID   0.000175137:0.00244577:0.0255279:0.868988:25697:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.127576 ES:SE:LP:AF:SS:ID   -0.000298487:0.00249428:0.0434733:0.127576:25697:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.868487 ES:SE:LP:AF:SS:ID   0.000163847:0.00243941:0.0239023:0.868487:25697:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867984 ES:SE:LP:AF:SS:ID   0.000231792:0.00243925:0.0341894:0.867984:25697:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868672 ES:SE:LP:AF:SS:ID   0.000297942:0.00244132:0.0443762:0.868672:25697:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.868677 ES:SE:LP:AF:SS:ID   0.000296129:0.00244147:0.0440906:0.868677:25697:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.868677 ES:SE:LP:AF:SS:ID   0.000301334:0.00244147:0.0449028:0.868677:25697:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.86918  ES:SE:LP:AF:SS:ID   6.40276e-05:0.00244873:0.00915521:0.86918:25697:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.09951  ES:SE:LP:AF:SS:ID   7.15336e-05:0.00285338:0.00877437:0.09951:25697:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00646211   ES:SE:LP:AF:SS:ID   0.00899133:0.0109337:0.386278:0.00646211:25697:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873078 ES:SE:LP:AF:SS:ID   0.000137102:0.00248303:0.0195571:0.873078:25697:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.862503 ES:SE:LP:AF:SS:ID   -0.000160023:0.00243629:0.0233607:0.862503:25697:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.867879 ES:SE:LP:AF:SS:ID   -3.19077e-05:0.00245992:0.00451793:0.867879:25697:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.86493  ES:SE:LP:AF:SS:ID   0.000244954:0.00246263:0.0358503:0.86493:25697:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0994497    ES:SE:LP:AF:SS:ID   0.00146245:0.00295035:0.207524:0.0994497:25697:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870274 ES:SE:LP:AF:SS:ID   2.99964e-05:0.00249071:0.00419331:0.870274:25697:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870274 ES:SE:LP:AF:SS:ID   2.99994e-05:0.00249071:0.00419375:0.870274:25697:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870271 ES:SE:LP:AF:SS:ID   3.04688e-05:0.00249072:0.00425953:0.870271:25697:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.870593 ES:SE:LP:AF:SS:ID   -0.000226332:0.00249123:0.0326341:0.870593:25697:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.12718  ES:SE:LP:AF:SS:ID   -0.000289556:0.00249177:0.0421577:0.12718:25697:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106621 ES:SE:LP:AF:SS:ID   0.000473543:0.00270874:0.0648849:0.106621:25697:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.853855 ES:SE:LP:AF:SS:ID   2.86792e-05:0.00242879:0.00411088:0.853855:25697:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00184915   ES:SE:LP:AF:SS:ID   0.00466487:0.0201644:0.0877508:0.00184915:25697:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838998 ES:SE:LP:AF:SS:ID   0.00130286:0.00240717:0.230366:0.838998:25697:rs376645387