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-5779/ukb-d-5779.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-5779/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 17:15:57 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-5779/ukb-d-5779.vcf.gz ...
Read summary statistics for 13068167 SNPs.
Dropped 11965 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, 1283397 SNPs remain.
After merging with regression SNP LD, 1283397 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.099 (0.0356)
Lambda GC: 1.0197
Mean Chi^2: 1.022
Intercept: 0.9977 (0.0057)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 17:18:36 2019
Total time elapsed: 2.0m:38.83s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9512,
    "inflation_factor": 1.0137,
    "mean_EFFECT": -0,
    "n": 12493,
    "n_snps": 13068167,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 1231858,
    "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": 447343,
    "n_est": 12582.5658,
    "ratio_se_n": 1.0036,
    "mean_diff": -0,
    "ratio_diff": 216.7666,
    "sd_y_est1": 0.4521,
    "sd_y_est2": 0.4537,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1283397,
    "ldsc_nsnp_merge_regression_ld": 1283397,
    "ldsc_observed_scale_h2_beta": 0.099,
    "ldsc_observed_scale_h2_se": 0.0356,
    "ldsc_intercept_beta": 0.9977,
    "ldsc_intercept_se": 0.0057,
    "ldsc_lambda_gc": 1.0197,
    "ldsc_mean_chisq": 1.022,
    "ldsc_ratio": -0.1045
}

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 13056854 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 56000 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33006 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.057372e+00 6.184798e+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 7.897783e+07 5.593499e+07 3.02000e+02 3.284420e+07 7.005470e+07 1.148185e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -3.390000e-05 2.939170e-02 -3.66791e-01 -8.357500e-03 4.320000e-05 8.482400e-03 3.455480e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 2.154450e-02 1.988570e-02 4.62410e-03 6.751200e-03 1.181980e-02 3.061250e-02 1.223780e-01 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.979341e-01 2.891777e-01 2.00000e-07 2.472125e-01 4.970854e-01 7.480911e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.979213e-01 2.891854e-01 2.00000e-07 2.471904e-01 4.970722e-01 7.480859e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.980011e-01 2.561392e-01 1.39780e-03 9.405300e-03 6.934740e-02 3.096310e-01 9.986020e-01 ▇▂▁▁▁
numeric AF_reference 447343 0.9657685 NA NA NA NA NA 2.018899e-01 2.483340e-01 0.00000e+00 6.988800e-03 9.185300e-02 3.184900e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 1.249300e+04 0.000000e+00 1.24930e+04 1.249300e+04 1.249300e+04 1.249300e+04 1.249300e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0151602 0.0099505 0.1276430 0.1276183 0.1119700 0.1894970 12493
1 693731 rs12238997 A G 0.0149650 0.0095134 0.1157329 0.1157074 0.1164160 0.1417730 12493
1 707522 rs371890604 G C 0.0215420 0.0106206 0.0425491 0.0425274 0.0987938 0.1293930 12493
1 717587 rs144155419 G A -0.0034522 0.0254909 0.8922760 0.8922735 0.0156789 0.0045926 12493
1 723329 rs189787166 A T 0.0543563 0.0663675 0.4127908 0.4127751 0.0021294 0.0003994 12493
1 730087 rs148120343 T C 0.0358477 0.0132304 0.0067479 0.0067386 0.0564497 0.0127796 12493
1 731718 rs142557973 T C 0.0121053 0.0090285 0.1800110 0.1799885 0.1224830 0.1543530 12493
1 732032 rs61770163 A C 0.0181872 0.0095964 0.0580871 0.0580640 0.1221340 0.1555510 12493
1 734349 rs141242758 T C 0.0114675 0.0090368 0.2044702 0.2044469 0.1216000 0.1525560 12493
1 740284 rs61770167 C T 0.0245715 0.0409673 0.5486621 0.5486507 0.0059632 0.0023962 12493
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0041794 0.0102868 0.6845418 0.6845348 0.0557540 0.0309934 12493
23 154923374 rs111332691 T A -0.0102953 0.0113067 0.3625517 0.3625333 0.0451453 0.0116556 12493
23 154925045 rs509981 C T 0.0026171 0.0054031 0.6281307 0.6281227 0.2466490 0.3634440 12493
23 154925895 rs538470 C T 0.0029559 0.0055227 0.5925022 0.5924926 0.2433070 0.3634440 12493
23 154927581 rs644138 G A 0.0014846 0.0051013 0.7710384 0.7710335 0.3026160 0.4635760 12493
23 154929412 rs557132 C T 0.0027296 0.0054048 0.6135444 0.6135353 0.2465100 0.3568210 12493
23 154929637 rs35185538 CT C 0.0018234 0.0056450 0.7466911 0.7466860 0.2303590 0.3011920 12493
23 154929952 rs4012982 CAA C 0.0033917 0.0056710 0.5498041 0.5497931 0.2410230 0.3165560 12493
23 154930230 rs781880 A G 0.0029471 0.0054039 0.5855177 0.5855087 0.2470010 0.3618540 12493
23 154930487 rs781879 T A 0.0279935 0.0185922 0.1321810 0.1321549 0.0198284 0.1263580 12493

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.11197  ES:SE:LP:AF:SS:ID   0.0151602:0.0099505:0.894003:0.11197:12493:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116416 ES:SE:LP:AF:SS:ID   0.014965:0.00951337:0.936543:0.116416:12493:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0987938    ES:SE:LP:AF:SS:ID   0.021542:0.0106206:1.37111:0.0987938:12493:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0156789    ES:SE:LP:AF:SS:ID   -0.00345218:0.0254909:0.0495008:0.0156789:12493:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00212935   ES:SE:LP:AF:SS:ID   0.0543563:0.0663675:0.38427:0.00212935:12493:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0564497    ES:SE:LP:AF:SS:ID   0.0358477:0.0132304:2.17083:0.0564497:12493:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122483 ES:SE:LP:AF:SS:ID   0.0121053:0.00902848:0.744701:0.122483:12493:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122134 ES:SE:LP:AF:SS:ID   0.0181872:0.00959638:1.23592:0.122134:12493:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.1216   ES:SE:LP:AF:SS:ID   0.0114675:0.00903676:0.68937:0.1216:12493:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00596318   ES:SE:LP:AF:SS:ID   0.0245715:0.0409673:0.260695:0.00596318:12493:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00154879   ES:SE:LP:AF:SS:ID   0.117695:0.0882834:0.738723:0.00154879:12493:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869199 ES:SE:LP:AF:SS:ID   -0.00950411:0.00891011:0.543417:0.869199:12493:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00156621   ES:SE:LP:AF:SS:ID   0.0618386:0.07948:0.359956:0.00156621:12493:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.124137 ES:SE:LP:AF:SS:ID   0.0101481:0.00879231:0.60478:0.124137:12493:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143472 ES:SE:LP:AF:SS:ID   0.00790268:0.00871037:0.438565:0.143472:12493:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.124212 ES:SE:LP:AF:SS:ID   0.0100419:0.00878193:0.59711:0.124212:12493:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.87003  ES:SE:LP:AF:SS:ID   -0.00972417:0.00856167:0.59164:0.87003:12493:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874482 ES:SE:LP:AF:SS:ID   -0.0106575:0.00869788:0.656621:0.874482:12493:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.129704 ES:SE:LP:AF:SS:ID   0.00981665:0.00858005:0.597579:0.129704:12493:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0361002    ES:SE:LP:AF:SS:ID   0.00888709:0.0155755:0.245428:0.0361002:12493:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869671 ES:SE:LP:AF:SS:ID   -0.0104051:0.00855372:0.650064:0.869671:12493:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869765 ES:SE:LP:AF:SS:ID   -0.0102138:0.00855917:0.633073:0.869765:12493:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869671 ES:SE:LP:AF:SS:ID   -0.0104101:0.00855344:0.650524:0.869671:12493:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00521186   ES:SE:LP:AF:SS:ID   0.0169138:0.0426847:0.15994:0.00521186:12493:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00518424   ES:SE:LP:AF:SS:ID   0.0168487:0.0427972:0.158754:0.00518424:12493:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00501504   ES:SE:LP:AF:SS:ID   -0.0261336:0.0449389:0.251123:0.00501504:12493:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.869743 ES:SE:LP:AF:SS:ID   -0.00965702:0.00853916:0.588187:0.869743:12493:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126893 ES:SE:LP:AF:SS:ID   0.0110494:0.00872021:0.687943:0.126893:12493:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.869367 ES:SE:LP:AF:SS:ID   -0.0100309:0.00852373:0.621075:0.869367:12493:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868739 ES:SE:LP:AF:SS:ID   -0.00925294:0.00851907:0.556837:0.868739:12493:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869534 ES:SE:LP:AF:SS:ID   -0.00970038:0.00852915:0.592735:0.869534:12493:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.86954  ES:SE:LP:AF:SS:ID   -0.00970215:0.0085297:0.59283:0.86954:12493:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869558 ES:SE:LP:AF:SS:ID   -0.00980439:0.00853042:0.601303:0.869558:12493:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869972 ES:SE:LP:AF:SS:ID   -0.00948883:0.00855309:0.573037:0.869972:12493:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.100048 ES:SE:LP:AF:SS:ID   0.0124044:0.00993525:0.673947:0.100048:12493:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00584704   ES:SE:LP:AF:SS:ID   -0.0326227:0.0407705:0.373006:0.00584704:12493:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873827 ES:SE:LP:AF:SS:ID   -0.0106066:0.00867567:0.654593:0.873827:12493:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863361 ES:SE:LP:AF:SS:ID   -0.00940125:0.00852255:0.56863:0.863361:12493:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868808 ES:SE:LP:AF:SS:ID   -0.0103188:0.00858866:0.63903:0.868808:12493:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865581 ES:SE:LP:AF:SS:ID   -0.00967147:0.00859035:0.584611:0.865581:12493:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0997109    ES:SE:LP:AF:SS:ID   0.00643791:0.0103048:0.273967:0.0997109:12493:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870747 ES:SE:LP:AF:SS:ID   -0.010776:0.00867776:0.668909:0.870747:12493:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870747 ES:SE:LP:AF:SS:ID   -0.010776:0.00867776:0.668909:0.870747:12493:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870785 ES:SE:LP:AF:SS:ID   -0.0106697:0.00868166:0.659362:0.870785:12493:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871078 ES:SE:LP:AF:SS:ID   -0.0111181:0.00868658:0.697669:0.871078:12493:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126652 ES:SE:LP:AF:SS:ID   0.00986153:0.00869817:0.590199:0.126652:12493:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105802 ES:SE:LP:AF:SS:ID   0.0107754:0.00951106:0.589624:0.105802:12493:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.853994 ES:SE:LP:AF:SS:ID   -0.00646635:0.00851796:0.348934:0.853994:12493:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00202513   ES:SE:LP:AF:SS:ID   0.0555003:0.0664861:0.393764:0.00202513:12493:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838577 ES:SE:LP:AF:SS:ID   0.00693534:0.00833259:0.39228:0.838577:12493:rs376645387