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-Z12/ukb-d-Z12.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-Z12/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 17:48:39 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-Z12/ukb-d-Z12.vcf.gz ...
Read summary statistics for 11748065 SNPs.
Dropped 10104 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, 1282818 SNPs remain.
After merging with regression SNP LD, 1282818 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0008 (0.0013)
Lambda GC: 1.0152
Mean Chi^2: 1.0173
Intercept: 1.0112 (0.0063)
Ratio: 0.6498 (0.3633)
Analysis finished at Mon Nov 25 17:50:34 2019
Total time elapsed: 1.0m:54.79s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9483,
    "inflation_factor": 1.0069,
    "mean_EFFECT": -6.4729e-07,
    "n": 361194,
    "n_snps": 11748065,
    "n_clumped_hits": 0,
    "n_p_sig": 1,
    "n_mono": 0,
    "n_ns": 1173071,
    "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": 261046,
    "n_est": 361991.784,
    "ratio_se_n": 1.0011,
    "mean_diff": -5.06e-06,
    "ratio_diff": 17.9002,
    "sd_y_est1": 0.1079,
    "sd_y_est2": 0.1081,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1282818,
    "ldsc_nsnp_merge_regression_ld": 1282818,
    "ldsc_observed_scale_h2_beta": 0.0008,
    "ldsc_observed_scale_h2_se": 0.0013,
    "ldsc_intercept_beta": 1.0112,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.0152,
    "ldsc_mean_chisq": 1.0173,
    "ldsc_ratio": 0.6474
}

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 TRUE
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 11738575 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 52808 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 31934 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.071543e+00 6.184499e+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 7.887308e+07 5.598709e+07 3.02000e+02 3.267173e+07 6.989648e+07 1.147353e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -6.000000e-07 9.367000e-04 -1.00565e-02 -3.363000e-04 -4.300000e-06 3.263000e-04 1.326770e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 7.301000e-04 5.854000e-04 2.09000e-04 2.904000e-04 4.510000e-04 9.999000e-04 3.571400e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.984794e-01 2.893282e-01 0.00000e+00 2.468113e-01 4.985159e-01 7.490641e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.984789e-01 2.893285e-01 0.00000e+00 2.468103e-01 4.985156e-01 7.490637e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.190420e-01 2.600750e-01 2.95300e-03 1.724540e-02 9.864160e-02 3.492540e-01 9.970470e-01 ▇▂▁▁▁
numeric AF_reference 261046 0.9777797 NA NA NA NA NA 2.200202e-01 2.514490e-01 0.00000e+00 1.417730e-02 1.176120e-01 3.502400e-01 1.000000e+00 ▇▂▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA 3.611940e+05 0.000000e+00 3.61194e+05 3.611940e+05 3.611940e+05 3.611940e+05 3.611940e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0001965 0.0004452 0.6589083 0.6589080 0.1106400 0.1894970 361194
1 693731 rs12238997 A G -0.0000890 0.0004206 0.8323650 0.8323643 0.1158300 0.1417730 361194
1 707522 rs371890604 G C -0.0003144 0.0004729 0.5060798 0.5060792 0.0973034 0.1293930 361194
1 717587 rs144155419 G A 0.0010986 0.0011284 0.3302707 0.3302687 0.0156880 0.0045926 361194
1 730087 rs148120343 T C 0.0000948 0.0005860 0.8714290 0.8714288 0.0564602 0.0127796 361194
1 731718 rs142557973 T C -0.0002205 0.0003989 0.5803634 0.5803635 0.1217380 0.1543530 361194
1 732032 rs61770163 A C -0.0003123 0.0004255 0.4630491 0.4630477 0.1211710 0.1555510 361194
1 734349 rs141242758 T C -0.0002338 0.0003991 0.5580884 0.5580879 0.1209650 0.1525560 361194
1 740284 rs61770167 C T -0.0020457 0.0018260 0.2625971 0.2625962 0.0057851 0.0023962 361194
1 749963 rs529266287 T TAA 0.0001625 0.0003935 0.6796589 0.6796585 0.8697420 0.7641770 361194
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0000318 0.0004609 0.9449480 0.9449483 0.0561667 0.0309934 361194
23 154923374 rs111332691 T A -0.0003517 0.0005065 0.4874555 0.4874551 0.0447862 0.0116556 361194
23 154925045 rs509981 C T -0.0002041 0.0002437 0.4022990 0.4022987 0.2456060 0.3634440 361194
23 154925895 rs538470 C T -0.0002660 0.0002493 0.2859243 0.2859242 0.2419150 0.3634440 361194
23 154927581 rs644138 G A -0.0001858 0.0002292 0.4175947 0.4175923 0.3021620 0.4635760 361194
23 154929412 rs557132 C T -0.0001979 0.0002438 0.4169299 0.4169293 0.2454590 0.3568210 361194
23 154929637 rs35185538 CT C -0.0003245 0.0002544 0.2021469 0.2021457 0.2296970 0.3011920 361194
23 154929952 rs4012982 CAA C -0.0002208 0.0002564 0.3891150 0.3891127 0.2394250 0.3165560 361194
23 154930230 rs781880 A G -0.0001989 0.0002437 0.4145759 0.4145732 0.2458690 0.3618540 361194
23 154930487 rs781879 T A -0.0000899 0.0008361 0.9143501 0.9143500 0.0195623 0.1263580 361194

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.11064  ES:SE:LP:AF:SS:ID   0.000196504:0.000445162:0.181175:0.11064:361194:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.11583  ES:SE:LP:AF:SS:ID   -8.90336e-05:0.000420624:0.0796862:0.11583:361194:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0973034    ES:SE:LP:AF:SS:ID   -0.000314436:0.000472868:0.295781:0.0973034:361194:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.015688 ES:SE:LP:AF:SS:ID   0.00109856:0.00112838:0.48113:0.015688:361194:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0564602    ES:SE:LP:AF:SS:ID   9.48381e-05:0.000585985:0.059768:0.0564602:361194:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121738 ES:SE:LP:AF:SS:ID   -0.000220545:0.000398921:0.2363:0.121738:361194:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121171 ES:SE:LP:AF:SS:ID   -0.000312267:0.000425526:0.334373:0.121171:361194:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.120965 ES:SE:LP:AF:SS:ID   -0.000233767:0.000399135:0.253297:0.120965:361194:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00578512   ES:SE:LP:AF:SS:ID   -0.00204568:0.00182605:0.58071:0.00578512:361194:rs61770167
1   749963  rs529266287 T   TAA .   PASS    AF=0.869742 ES:SE:LP:AF:SS:ID   0.000162481:0.000393484:0.167709:0.869742:361194:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.122916 ES:SE:LP:AF:SS:ID   -0.00016683:0.000389694:0.17485:0.122916:361194:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142712 ES:SE:LP:AF:SS:ID   -0.000108109:0.000385192:0.10848:0.142712:361194:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123031 ES:SE:LP:AF:SS:ID   -0.00017382:0.000389152:0.18368:0.123031:361194:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.87089  ES:SE:LP:AF:SS:ID   0.000142435:0.000379216:0.150451:0.87089:361194:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.875472 ES:SE:LP:AF:SS:ID   0.000196015:0.0003855:0.213871:0.875472:361194:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128631 ES:SE:LP:AF:SS:ID   -0.000148825:0.000380167:0.157736:0.128631:361194:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0363613    ES:SE:LP:AF:SS:ID   -0.000145816:0.00068971:0.0795829:0.0363613:361194:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870484 ES:SE:LP:AF:SS:ID   0.000159496:0.000378822:0.171513:0.870484:361194:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870589 ES:SE:LP:AF:SS:ID   0.000149879:0.000378994:0.159581:0.870589:361194:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870481 ES:SE:LP:AF:SS:ID   0.000159537:0.000378807:0.171571:0.870481:361194:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00504367   ES:SE:LP:AF:SS:ID   0.00247937:0.00194473:0.69392:0.00504367:361194:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00501064   ES:SE:LP:AF:SS:ID   0.00248089:0.00194993:0.691929:0.00501064:361194:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00558665   ES:SE:LP:AF:SS:ID   0.000130758:0.00188352:0.0247272:0.00558665:361194:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870563 ES:SE:LP:AF:SS:ID   0.000150377:0.000378141:0.160604:0.870563:361194:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.125985 ES:SE:LP:AF:SS:ID   -0.000184203:0.000385842:0.198545:0.125985:361194:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.870127 ES:SE:LP:AF:SS:ID   0.000148655:0.000377231:0.158934:0.870127:361194:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869414 ES:SE:LP:AF:SS:ID   0.000182529:0.000376887:0.201924:0.869414:361194:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870281 ES:SE:LP:AF:SS:ID   0.000156826:0.000377555:0.168854:0.870281:361194:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870287 ES:SE:LP:AF:SS:ID   0.00015687:0.000377582:0.168895:0.870287:361194:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870295 ES:SE:LP:AF:SS:ID   0.000162595:0.000377594:0.176034:0.870295:361194:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870739 ES:SE:LP:AF:SS:ID   0.000141106:0.000378556:0.149147:0.870739:361194:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0988442    ES:SE:LP:AF:SS:ID   -0.000299636:0.000439904:0.304707:0.0988442:361194:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00647588   ES:SE:LP:AF:SS:ID   0.000833135:0.00170157:0.204538:0.00647588:361194:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.874635 ES:SE:LP:AF:SS:ID   0.000152727:0.000384197:0.160533:0.874635:361194:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.864027 ES:SE:LP:AF:SS:ID   0.000116644:0.000376762:0.12098:0.864027:361194:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.869425 ES:SE:LP:AF:SS:ID   0.000213046:0.00038042:0.239985:0.869425:361194:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866371 ES:SE:LP:AF:SS:ID   0.0002201:0.000380497:0.249523:0.866371:361194:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0987831    ES:SE:LP:AF:SS:ID   -0.000176551:0.000455527:0.155939:0.0987831:361194:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.871556 ES:SE:LP:AF:SS:ID   0.000170432:0.000384832:0.181868:0.871556:361194:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.871556 ES:SE:LP:AF:SS:ID   0.000170438:0.000384832:0.181875:0.871556:361194:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.871555 ES:SE:LP:AF:SS:ID   0.000170136:0.00038484:0.181497:0.871555:361194:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871929 ES:SE:LP:AF:SS:ID   0.000174365:0.000385059:0.186637:0.871929:361194:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125562 ES:SE:LP:AF:SS:ID   -0.000170029:0.000385363:0.181078:0.125562:361194:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105428 ES:SE:LP:AF:SS:ID   -0.000427625:0.00041952:0.511376:0.105428:361194:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855376 ES:SE:LP:AF:SS:ID   0.000219881:0.000376621:0.252326:0.855376:361194:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838358 ES:SE:LP:AF:SS:ID   -0.00027513:0.000371039:0.338771:0.838358:361194:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.763351 ES:SE:LP:AF:SS:ID   -0.000129076:0.000298617:0.176811:0.763351:361194:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.105921 ES:SE:LP:AF:SS:ID   0.000404651:0.000411421:0.487663:0.105921:361194:rs12562034
1   768819  rs12562811  C   T   .   PASS    AF=0.00768395   ES:SE:LP:AF:SS:ID   -0.000906725:0.00152862:0.25722:0.00768395:361194:rs12562811
1   769138  rs59306077  CAT C   .   PASS    AF=0.128938 ES:SE:LP:AF:SS:ID   -0.000205239:0.000379972:0.229812:0.128938:361194:rs762168062