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-30020_irnt/ukb-d-30020_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-30020_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 14:51:50 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-30020_irnt/ukb-d-30020_irnt.vcf.gz ...
Read summary statistics for 13586286 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: 0.1452 (0.0096)
Lambda GC: 1.5829
Mean Chi^2: 2.1825
Intercept: 1.1749 (0.0175)
Ratio: 0.1479 (0.0148)
Analysis finished at Mon Nov 25 14:53:55 2019
Total time elapsed: 2.0m:4.99s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.3591,
    "mean_EFFECT": -0,
    "n": 350474,
    "n_snps": 13586286,
    "n_clumped_hits": 298,
    "n_p_sig": 71099,
    "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": 569191,
    "n_est": 351810.3195,
    "ratio_se_n": 1.0019,
    "mean_diff": -7.5499e-06,
    "ratio_diff": 24.3021,
    "sd_y_est1": 0.7947,
    "sd_y_est2": 0.7962,
    "r2_sum1": 0.0475,
    "r2_sum2": 0.0751,
    "r2_sum3": 0.0749,
    "r2_sum4": 0.0748,
    "ldsc_nsnp_merge_refpanel_ld": 1283504,
    "ldsc_nsnp_merge_regression_ld": 1283504,
    "ldsc_observed_scale_h2_beta": 0.1452,
    "ldsc_observed_scale_h2_se": 0.0096,
    "ldsc_intercept_beta": 1.1749,
    "ldsc_intercept_se": 0.0175,
    "ldsc_lambda_gc": 1.5829,
    "ldsc_mean_chisq": 2.1825,
    "ldsc_ratio": 0.1479
}

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 13574240 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 9.051336e+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 5.591483e+07 3.02000e+02 3.293108e+07 7.013884e+07 1.148590e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -2.830000e-05 1.180070e-02 -2.37878e-01 -3.606800e-03 -1.710000e-05 3.590500e-03 2.129210e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 7.976400e-03 7.694300e-03 1.56010e-03 2.270900e-03 4.184700e-03 1.146290e-02 3.900820e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.487236e-01 3.038789e-01 0.00000e+00 1.723721e-01 4.316701e-01 7.124018e-01 1.000000e+00 ▇▆▅▅▅
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.487232e-01 3.038791e-01 0.00000e+00 1.723707e-01 4.316698e-01 7.124008e-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 6.003380e-02 2.953170e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 569191 0.9581055 NA NA NA NA NA 1.963356e-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 3.504740e+05 0.000000e+00 3.50474e+05 3.504740e+05 3.504740e+05 3.504740e+05 3.504740e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0060919 0.0033230 0.0667636 0.0667624 0.1106770 0.1894970 350474
1 693731 rs12238997 A G -0.0068418 0.0031398 0.0293292 0.0293290 0.1158570 0.1417730 350474
1 707522 rs371890604 G C -0.0047931 0.0035310 0.1746369 0.1746363 0.0972839 0.1293930 350474
1 717587 rs144155419 G A -0.0178466 0.0084159 0.0339571 0.0339560 0.0157161 0.0045926 350474
1 723329 rs189787166 A T 0.0118464 0.0249400 0.6347913 0.6347898 0.0017252 0.0003994 350474
1 730087 rs148120343 T C -0.0057232 0.0043729 0.1906062 0.1906054 0.0564938 0.0127796 350474
1 731718 rs142557973 T C -0.0062346 0.0029777 0.0362827 0.0362823 0.1217800 0.1543530 350474
1 732032 rs61770163 A C -0.0058262 0.0031768 0.0666546 0.0666533 0.1211750 0.1555510 350474
1 734349 rs141242758 T C -0.0063617 0.0029793 0.0327378 0.0327370 0.1210090 0.1525560 350474
1 740284 rs61770167 C T 0.0133995 0.0136307 0.3255892 0.3255886 0.0057871 0.0023962 350474
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0008248 0.0034395 0.8104791 0.8104786 0.0561532 0.0309934 350474
23 154923374 rs111332691 T A 0.0007669 0.0037810 0.8392580 0.8392580 0.0447908 0.0116556 350474
23 154925045 rs509981 C T -0.0060416 0.0018195 0.0008985 0.0008984 0.2454620 0.3634440 350474
23 154925895 rs538470 C T -0.0059489 0.0018611 0.0013912 0.0013911 0.2418090 0.3634440 350474
23 154927581 rs644138 G A -0.0051383 0.0017109 0.0026712 0.0026709 0.3020110 0.4635760 350474
23 154929412 rs557132 C T -0.0060414 0.0018200 0.0009020 0.0009019 0.2453170 0.3568210 350474
23 154929637 rs35185538 CT C -0.0057003 0.0018997 0.0026943 0.0026942 0.2295390 0.3011920 350474
23 154929952 rs4012982 CAA C -0.0063525 0.0019142 0.0009050 0.0009049 0.2392720 0.3165560 350474
23 154930230 rs781880 A G -0.0060308 0.0018197 0.0009191 0.0009190 0.2457280 0.3618540 350474
23 154930487 rs781879 T A -0.0055434 0.0062447 0.3747098 0.3747095 0.0195298 0.1263580 350474

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.110677 ES:SE:LP:AF:SS:ID   -0.00609192:0.00332298:1.17546:0.110677:350474:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.115857 ES:SE:LP:AF:SS:ID   -0.00684178:0.00313982:1.5327:0.115857:350474:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0972839    ES:SE:LP:AF:SS:ID   -0.00479311:0.00353095:0.757864:0.0972839:350474:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157161    ES:SE:LP:AF:SS:ID   -0.0178466:0.00841585:1.46907:0.0157161:350474:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00172518   ES:SE:LP:AF:SS:ID   0.0118464:0.02494:0.197369:0.00172518:350474:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0564938    ES:SE:LP:AF:SS:ID   -0.00572317:0.00437287:0.719863:0.0564938:350474:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12178  ES:SE:LP:AF:SS:ID   -0.00623458:0.00297771:1.4403:0.12178:350474:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121175 ES:SE:LP:AF:SS:ID   -0.00582621:0.00317677:1.17617:0.121175:350474:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121009 ES:SE:LP:AF:SS:ID   -0.00636168:0.0029793:1.48495:0.121009:350474:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00578711   ES:SE:LP:AF:SS:ID   0.0133995:0.0136307:0.48733:0.00578711:350474:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00188409   ES:SE:LP:AF:SS:ID   0.00775273:0.0257261:0.117394:0.00188409:350474:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869714 ES:SE:LP:AF:SS:ID   0.00612141:0.00293738:1.42988:0.869714:350474:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00153227   ES:SE:LP:AF:SS:ID   0.0635989:0.0265728:1.77743:0.00153227:350474:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.122937 ES:SE:LP:AF:SS:ID   -0.00601051:0.00290892:1.41109:0.122937:350474:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142738 ES:SE:LP:AF:SS:ID   -0.00409858:0.00287551:0.812313:0.142738:350474:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123054 ES:SE:LP:AF:SS:ID   -0.00601777:0.00290485:1.41679:0.123054:350474:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.87085  ES:SE:LP:AF:SS:ID   0.00516616:0.00283067:1.16754:0.87085:350474:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.87545  ES:SE:LP:AF:SS:ID   0.00629267:0.00287761:1.54121:0.87545:350474:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128671 ES:SE:LP:AF:SS:ID   -0.005475:0.00283777:1.27011:0.128671:350474:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0363379    ES:SE:LP:AF:SS:ID   0.010394:0.00515218:1.35996:0.0363379:350474:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870444 ES:SE:LP:AF:SS:ID   0.00539702:0.0028277:1.24941:0.870444:350474:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870545 ES:SE:LP:AF:SS:ID   0.00527039:0.00282894:1.2044:0.870545:350474:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870439 ES:SE:LP:AF:SS:ID   0.00544608:0.00282758:1.26682:0.870439:350474:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00504296   ES:SE:LP:AF:SS:ID   -0.0176763:0.0145258:0.650439:0.00504296:350474:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00500983   ES:SE:LP:AF:SS:ID   -0.0179751:0.0145645:0.66326:0.00500983:350474:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00558056   ES:SE:LP:AF:SS:ID   0.00887042:0.0140692:0.277055:0.00558056:350474:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870523 ES:SE:LP:AF:SS:ID   0.00511025:0.00282263:1.1535:0.870523:350474:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126014 ES:SE:LP:AF:SS:ID   -0.00566648:0.00288034:1.30847:0.126014:350474:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.870086 ES:SE:LP:AF:SS:ID   0.00517685:0.00281582:1.18051:0.870086:350474:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869364 ES:SE:LP:AF:SS:ID   0.0050103:0.00281315:1.12546:0.869364:350474:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.87024  ES:SE:LP:AF:SS:ID   0.00511415:0.00281826:1.15752:0.87024:350474:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870246 ES:SE:LP:AF:SS:ID   0.00512069:0.00281846:1.15963:0.870246:350474:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870255 ES:SE:LP:AF:SS:ID   0.00511188:0.00281855:1.15657:0.870255:350474:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870697 ES:SE:LP:AF:SS:ID   0.00532545:0.00282572:1.22563:0.870697:350474:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0988383    ES:SE:LP:AF:SS:ID   -0.00480811:0.00328448:0.843978:0.0988383:350474:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00647156   ES:SE:LP:AF:SS:ID   -0.00114493:0.0127107:0.0323458:0.00647156:350474:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.874605 ES:SE:LP:AF:SS:ID   0.00629178:0.0028679:1.54905:0.874605:350474:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863994 ES:SE:LP:AF:SS:ID   0.00472956:0.00281239:1.03325:0.863994:350474:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.869378 ES:SE:LP:AF:SS:ID   0.0054149:0.00283967:1.24766:0.869378:350474:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866336 ES:SE:LP:AF:SS:ID   0.00563694:0.00284033:1.32616:0.866336:350474:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0987492    ES:SE:LP:AF:SS:ID   -0.00680302:0.00340141:1.34204:0.0987492:350474:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.87152  ES:SE:LP:AF:SS:ID   0.00643465:0.00287264:1.60045:0.87152:350474:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.87152  ES:SE:LP:AF:SS:ID   0.00643433:0.00287264:1.60032:0.87152:350474:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.871519 ES:SE:LP:AF:SS:ID   0.00641941:0.00287269:1.59444:0.871519:350474:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.87189  ES:SE:LP:AF:SS:ID   0.00641317:0.00287432:1.59059:0.87189:350474:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125595 ES:SE:LP:AF:SS:ID   -0.00587988:0.00287661:1.38774:0.125595:350474:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105424 ES:SE:LP:AF:SS:ID   -0.00360141:0.00313211:0.601692:0.105424:350474:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855335 ES:SE:LP:AF:SS:ID   0.00511117:0.00281166:1.1606:0.855335:350474:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00164752   ES:SE:LP:AF:SS:ID   0.0170252:0.0249832:0.304889:0.00164752:350474:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838288 ES:SE:LP:AF:SS:ID   -0.000892847:0.00276956:0.126583:0.838288:350474:rs376645387