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-30100_irnt/ukb-d-30100_irnt.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-30100_irnt/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 14:51:55 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-30100_irnt/ukb-d-30100_irnt.vcf.gz ...
Read summary statistics for 13586284 SNPs.
Dropped 12710 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, 1283504 SNPs remain.
After merging with regression SNP LD, 1283504 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.3459 (0.0444)
Lambda GC: 1.5526
Mean Chi^2: 3.5348
Intercept: 1.2308 (0.0233)
Ratio: 0.0911 (0.0092)
Analysis finished at Mon Nov 25 14:54:35 2019
Total time elapsed: 2.0m:40.68s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.3724,
    "mean_EFFECT": -0,
    "n": 350470,
    "n_snps": 13586284,
    "n_clumped_hits": 444,
    "n_p_sig": 133988,
    "n_mono": 0,
    "n_ns": 1251793,
    "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": 569192,
    "n_est": 351804.7002,
    "ratio_se_n": 1.0019,
    "mean_diff": 2.2263e-06,
    "ratio_diff": 1.0275,
    "sd_y_est1": 1.0026,
    "sd_y_est2": 1.0045,
    "r2_sum1": 0.2268,
    "r2_sum2": 0.2256,
    "r2_sum3": 0.2247,
    "r2_sum4": 0.2238,
    "ldsc_nsnp_merge_refpanel_ld": 1283504,
    "ldsc_nsnp_merge_regression_ld": 1283504,
    "ldsc_observed_scale_h2_beta": 0.3459,
    "ldsc_observed_scale_h2_se": 0.0444,
    "ldsc_intercept_beta": 1.2308,
    "ldsc_intercept_se": 0.0233,
    "ldsc_lambda_gc": 1.5526,
    "ldsc_mean_chisq": 3.5348,
    "ldsc_ratio": 0.0911
}

Flags

name value
af_correlation FALSE
inflation_factor TRUE
n FALSE
is_snpid_non_unique TRUE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq TRUE
n_p_sig TRUE
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 13574238 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 57080 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33337 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.051337e+00 6.184983e+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 7.902520e+07 5.591482e+07 3.02000e+02 3.293108e+07 7.013883e+07 1.148590e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -1.330000e-05 1.568850e-02 -4.94316e-01 -4.590600e-03 -1.140000e-05 4.556200e-03 7.593070e-01 ▁▇▂▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.006320e-02 9.707300e-03 1.96830e-03 2.865000e-03 5.279500e-03 1.446190e-02 4.921320e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.463910e-01 3.057725e-01 0.00000e+00 1.675089e-01 4.294415e-01 7.121738e-01 1.000000e+00 ▇▆▅▅▅
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.463905e-01 3.057727e-01 0.00000e+00 1.675081e-01 4.294407e-01 7.121734e-01 1.000000e+00 ▇▆▅▅▅
numeric AF 0 1.0000000 NA NA NA NA NA 1.908060e-01 2.544034e-01 1.00000e-03 7.430500e-03 6.003400e-02 2.953170e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 569192 0.9581054 NA NA NA NA NA 1.963357e-01 2.471015e-01 0.00000e+00 5.591000e-03 8.426520e-02 3.081070e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 3.504700e+05 0.000000e+00 3.50470e+05 3.504700e+05 3.504700e+05 3.504700e+05 3.504700e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0025648 0.0041924 0.5406896 0.5406893 0.1106750 0.1894970 350470
1 693731 rs12238997 A G -0.0032156 0.0039613 0.4169375 0.4169379 0.1158560 0.1417730 350470
1 707522 rs371890604 G C -0.0017880 0.0044548 0.6881435 0.6881439 0.0972819 0.1293930 350470
1 717587 rs144155419 G A -0.0088731 0.0106176 0.4033267 0.4033268 0.0157163 0.0045926 350470
1 723329 rs189787166 A T -0.0273622 0.0314646 0.3845103 0.3845089 0.0017252 0.0003994 350470
1 730087 rs148120343 T C -0.0013636 0.0055169 0.8047771 0.8047770 0.0564930 0.0127796 350470
1 731718 rs142557973 T C -0.0015674 0.0037568 0.6765206 0.6765213 0.1217780 0.1543530 350470
1 732032 rs61770163 A C -0.0035444 0.0040079 0.3765113 0.3765095 0.1211730 0.1555510 350470
1 734349 rs141242758 T C -0.0017499 0.0037588 0.6415345 0.6415329 0.1210080 0.1525560 350470
1 740284 rs61770167 C T -0.0084748 0.0171986 0.6221828 0.6221835 0.0057859 0.0023962 350470
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0017947 0.0043393 0.6791677 0.6791681 0.0561538 0.0309934 350470
23 154923374 rs111332691 T A -0.0087424 0.0047701 0.0668405 0.0668399 0.0447913 0.0116556 350470
23 154925045 rs509981 C T 0.0008815 0.0022955 0.7009564 0.7009551 0.2454620 0.3634440 350470
23 154925895 rs538470 C T 0.0000179 0.0023480 0.9939260 0.9939263 0.2418090 0.3634440 350470
23 154927581 rs644138 G A 0.0011819 0.0021586 0.5840042 0.5840022 0.3020110 0.4635760 350470
23 154929412 rs557132 C T 0.0008029 0.0022962 0.7265910 0.7265902 0.2453170 0.3568210 350470
23 154929637 rs35185538 CT C 0.0029822 0.0023967 0.2133949 0.2133936 0.2295390 0.3011920 350470
23 154929952 rs4012982 CAA C 0.0019601 0.0024151 0.4170105 0.4170091 0.2392720 0.3165560 350470
23 154930230 rs781880 A G 0.0009119 0.0022958 0.6912243 0.6912238 0.2457280 0.3618540 350470
23 154930487 rs781879 T A 0.0269591 0.0078783 0.0006218 0.0006218 0.0195298 0.1263580 350470

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.110675 ES:SE:LP:AF:SS:ID   -0.00256477:0.00419237:0.267052:0.110675:350470:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.115856 ES:SE:LP:AF:SS:ID   -0.00321555:0.00396128:0.379929:0.115856:350470:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0972819    ES:SE:LP:AF:SS:ID   -0.00178803:0.00445476:0.162321:0.0972819:350470:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157163    ES:SE:LP:AF:SS:ID   -0.00887307:0.0106176:0.394343:0.0157163:350470:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.0017252    ES:SE:LP:AF:SS:ID   -0.0273622:0.0314646:0.415092:0.0017252:350470:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.056493 ES:SE:LP:AF:SS:ID   -0.00136362:0.00551694:0.0943244:0.056493:350470:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121778 ES:SE:LP:AF:SS:ID   -0.00156738:0.00375677:0.169719:0.121778:350470:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121173 ES:SE:LP:AF:SS:ID   -0.00354437:0.0040079:0.424222:0.121173:350470:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121008 ES:SE:LP:AF:SS:ID   -0.00174992:0.00375877:0.19278:0.121008:350470:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00578594   ES:SE:LP:AF:SS:ID   -0.00847475:0.0171986:0.206082:0.00578594:350470:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00188268   ES:SE:LP:AF:SS:ID   0.0301805:0.0324733:0.452612:0.00188268:350470:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869716 ES:SE:LP:AF:SS:ID   0.002599:0.00370589:0.315958:0.869716:350470:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00153087   ES:SE:LP:AF:SS:ID   -0.057639:0.033543:1.06686:0.00153087:350470:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.122935 ES:SE:LP:AF:SS:ID   -0.00304512:0.00366998:0.39074:0.122935:350470:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142736 ES:SE:LP:AF:SS:ID   4.76351e-06:0.00362781:0.000455379:0.142736:350470:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123052 ES:SE:LP:AF:SS:ID   -0.0030913:0.00366484:0.399083:0.123052:350470:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.870853 ES:SE:LP:AF:SS:ID   0.00217119:0.00357126:0.265029:0.870853:350470:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.875452 ES:SE:LP:AF:SS:ID   0.0025637:0.00363049:0.318678:0.875452:350470:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128669 ES:SE:LP:AF:SS:ID   -0.00272729:0.0035802:0.350473:0.128669:350470:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0363383    ES:SE:LP:AF:SS:ID   0.00814066:0.00650007:0.676901:0.0363383:350470:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870447 ES:SE:LP:AF:SS:ID   0.0022492:0.00356751:0.277046:0.870447:350470:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870548 ES:SE:LP:AF:SS:ID   0.00218344:0.00356908:0.267049:0.870548:350470:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870442 ES:SE:LP:AF:SS:ID   0.00223311:0.00356736:0.274638:0.870442:350470:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00504302   ES:SE:LP:AF:SS:ID   -0.0292347:0.0183259:0.956041:0.00504302:350470:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00500989   ES:SE:LP:AF:SS:ID   -0.0299815:0.0183747:0.988218:0.00500989:350470:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00558062   ES:SE:LP:AF:SS:ID   0.0216837:0.0177499:0.653941:0.00558062:350470:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870526 ES:SE:LP:AF:SS:ID   0.00203835:0.00356111:0.246373:0.870526:350470:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126012 ES:SE:LP:AF:SS:ID   -0.00323882:0.00363392:0.428544:0.126012:350470:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.870089 ES:SE:LP:AF:SS:ID   0.00224181:0.00355253:0.277358:0.870089:350470:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869367 ES:SE:LP:AF:SS:ID   0.0022062:0.00354916:0.272298:0.869367:350470:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870243 ES:SE:LP:AF:SS:ID   0.00215115:0.0035556:0.263462:0.870243:350470:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870249 ES:SE:LP:AF:SS:ID   0.00215627:0.00355585:0.264202:0.870249:350470:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870258 ES:SE:LP:AF:SS:ID   0.00216886:0.00355597:0.266069:0.870258:350470:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.8707   ES:SE:LP:AF:SS:ID   0.00200505:0.00356501:0.241219:0.8707:350470:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.098836 ES:SE:LP:AF:SS:ID   -0.00103762:0.0041438:0.0956762:0.098836:350470:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00647163   ES:SE:LP:AF:SS:ID   0.0208449:0.016036:0.713005:0.00647163:350470:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.874608 ES:SE:LP:AF:SS:ID   0.00252798:0.00361823:0.314479:0.874608:350470:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863997 ES:SE:LP:AF:SS:ID   0.00249083:0.00354819:0.316341:0.863997:350470:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.86938  ES:SE:LP:AF:SS:ID   0.00248942:0.00358262:0.312344:0.86938:350470:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866339 ES:SE:LP:AF:SS:ID   0.00265126:0.00358345:0.337825:0.866339:350470:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.098748 ES:SE:LP:AF:SS:ID   -0.00349574:0.00429131:0.381641:0.098748:350470:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.871522 ES:SE:LP:AF:SS:ID   0.00310925:0.00362421:0.407888:0.871522:350470:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.871522 ES:SE:LP:AF:SS:ID   0.00310879:0.00362421:0.40781:0.871522:350470:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.871522 ES:SE:LP:AF:SS:ID   0.00312414:0.00362428:0.4104:0.871522:350470:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871893 ES:SE:LP:AF:SS:ID   0.00306198:0.00362633:0.399614:0.871893:350470:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125593 ES:SE:LP:AF:SS:ID   -0.00299167:0.00362921:0.38748:0.125593:350470:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105422 ES:SE:LP:AF:SS:ID   -0.00104124:0.00395155:0.101185:0.105422:350470:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855338 ES:SE:LP:AF:SS:ID   0.00191169:0.00354727:0.229191:0.855338:350470:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00164753   ES:SE:LP:AF:SS:ID   -0.0151934:0.0315191:0.200811:0.00164753:350470:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.83829  ES:SE:LP:AF:SS:ID   -0.000699404:0.00349415:0.0750223:0.83829:350470:rs376645387