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-12336_irnt/ukb-d-12336_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-12336_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 14:52:11 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-12336_irnt/ukb-d-12336_irnt.vcf.gz ...
Read summary statistics for 13479254 SNPs.
Dropped 12539 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, 1283473 SNPs remain.
After merging with regression SNP LD, 1283473 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.184 (0.0443)
Lambda GC: 1.0311
Mean Chi^2: 1.0364
Intercept: 0.9971 (0.0063)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 14:54:15 2019
Total time elapsed: 2.0m:3.83s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9519,
    "inflation_factor": 1.0205,
    "mean_EFFECT": 0.0001,
    "n": 10817,
    "n_snps": 13479254,
    "n_clumped_hits": 1,
    "n_p_sig": 2,
    "n_mono": 0,
    "n_ns": 1247922,
    "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": 544276,
    "n_est": 10901.4077,
    "ratio_se_n": 1.0039,
    "mean_diff": -1.58e-06,
    "ratio_diff": 0.8581,
    "sd_y_est1": 0.9937,
    "sd_y_est2": 0.9976,
    "r2_sum1": 0.0029,
    "r2_sum2": 0.0029,
    "r2_sum3": 0.0029,
    "r2_sum4": 0.0029,
    "ldsc_nsnp_merge_refpanel_ld": 1283473,
    "ldsc_nsnp_merge_regression_ld": 1283473,
    "ldsc_observed_scale_h2_beta": 0.184,
    "ldsc_observed_scale_h2_se": 0.0443,
    "ldsc_intercept_beta": 0.9971,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.0311,
    "ldsc_mean_chisq": 1.0364,
    "ldsc_ratio": -0.0797
}

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 13467377 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 56884 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33286 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.052437e+00 6.184539e+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 7.901823e+07 5.591892e+07 3.02000e+02 3.291159e+07 7.012668e+07 1.148529e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 1.080000e-04 7.710730e-02 -9.51109e-01 -2.074170e-02 -4.230000e-05 2.084840e-02 9.780340e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 5.557130e-02 5.335800e-02 1.09673e-02 1.611880e-02 2.937080e-02 7.953400e-02 3.080070e-01 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.968411e-01 2.894686e-01 0.00000e+00 2.460441e-01 4.956288e-01 7.475513e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.968263e-01 2.894774e-01 0.00000e+00 2.460183e-01 4.956141e-01 7.475451e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.922283e-01 2.547596e-01 1.00010e-03 7.807800e-03 6.181410e-02 2.981820e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 544276 0.9596212 NA NA NA NA NA 1.974390e-01 2.473497e-01 0.00000e+00 5.990400e-03 8.586260e-02 3.103040e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 1.081700e+04 0.000000e+00 1.08170e+04 1.081700e+04 1.081700e+04 1.081700e+04 1.081700e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0049972 0.0233294 0.8303930 0.8303890 0.1140150 0.1894970 10817
1 693731 rs12238997 A G -0.0040950 0.0220958 0.8529740 0.8529704 0.1199670 0.1417730 10817
1 707522 rs371890604 G C -0.0019852 0.0246818 0.9358960 0.9358945 0.1008940 0.1293930 10817
1 717587 rs144155419 G A 0.0036825 0.0575685 0.9489980 0.9489966 0.0171602 0.0045926 10817
1 723329 rs189787166 A T -0.1194230 0.1606590 0.4572935 0.4572806 0.0020659 0.0003994 10817
1 730087 rs148120343 T C -0.0312555 0.0306532 0.3079179 0.3078950 0.0587969 0.0127796 10817
1 731718 rs142557973 T C 0.0060555 0.0208523 0.7715143 0.7715091 0.1265400 0.1543530 10817
1 732032 rs61770163 A C 0.0094026 0.0221636 0.6714026 0.6713936 0.1262890 0.1555510 10817
1 734349 rs141242758 T C 0.0061881 0.0208632 0.7667751 0.7667695 0.1258410 0.1525560 10817
1 740284 rs61770167 C T 0.0341534 0.0987562 0.7294726 0.7294663 0.0056284 0.0023962 10817
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0126600 0.0241550 0.6002111 0.6001985 0.0570406 0.0309934 10817
23 154923374 rs111332691 T A 0.0248514 0.0271613 0.3602326 0.3602146 0.0427568 0.0116556 10817
23 154925045 rs509981 C T 0.0097075 0.0128662 0.4505676 0.4505513 0.2438490 0.3634440 10817
23 154925895 rs538470 C T 0.0123417 0.0131378 0.3475442 0.3475234 0.2401380 0.3634440 10817
23 154927581 rs644138 G A 0.0060825 0.0120706 0.6143360 0.6143267 0.3011700 0.4635760 10817
23 154929412 rs557132 C T 0.0103997 0.0128698 0.4190656 0.4190503 0.2435800 0.3568210 10817
23 154929637 rs35185538 CT C 0.0109991 0.0134879 0.4148127 0.4147981 0.2261240 0.3011920 10817
23 154929952 rs4012982 CAA C 0.0151007 0.0135154 0.2638931 0.2638679 0.2381200 0.3165560 10817
23 154930230 rs781880 A G 0.0105757 0.0128573 0.4107845 0.4107671 0.2445570 0.3618540 10817
23 154930487 rs781879 T A 0.0408707 0.0442892 0.3561246 0.3561041 0.0189576 0.1263580 10817

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.114015 ES:SE:LP:AF:SS:ID   0.00499722:0.0233294:0.0807163:0.114015:10817:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.119967 ES:SE:LP:AF:SS:ID   -0.00409501:0.0220958:0.0690642:0.119967:10817:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.100894 ES:SE:LP:AF:SS:ID   -0.00198518:0.0246818:0.0287724:0.100894:10817:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0171602    ES:SE:LP:AF:SS:ID   0.00368248:0.0575685:0.0227347:0.0171602:10817:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00206592   ES:SE:LP:AF:SS:ID   -0.119423:0.160659:0.339805:0.00206592:10817:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0587969    ES:SE:LP:AF:SS:ID   -0.0312555:0.0306532:0.511565:0.0587969:10817:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12654  ES:SE:LP:AF:SS:ID   0.00605554:0.0208523:0.112656:0.12654:10817:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.126289 ES:SE:LP:AF:SS:ID   0.0094026:0.0221636:0.173017:0.126289:10817:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.125841 ES:SE:LP:AF:SS:ID   0.00618809:0.0208632:0.115332:0.125841:10817:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.0056284    ES:SE:LP:AF:SS:ID   0.0341534:0.0987562:0.136991:0.0056284:10817:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00164048   ES:SE:LP:AF:SS:ID   0.0161015:0.20043:0.0287371:0.00164048:10817:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.864204 ES:SE:LP:AF:SS:ID   -0.00918592:0.0205004:0.184355:0.864204:10817:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00183625   ES:SE:LP:AF:SS:ID   -0.249551:0.164551:0.888039:0.00183625:10817:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.128053 ES:SE:LP:AF:SS:ID   0.0135702:0.0203261:0.297236:0.128053:10817:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.146282 ES:SE:LP:AF:SS:ID   0.00684186:0.0201899:0.133884:0.146282:10817:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.128242 ES:SE:LP:AF:SS:ID   0.012571:0.020294:0.271129:0.128242:10817:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.865496 ES:SE:LP:AF:SS:ID   -0.0136163:0.0197862:0.308599:0.865496:10817:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.870096 ES:SE:LP:AF:SS:ID   -0.0157286:0.0200947:0.362704:0.870096:10817:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.133956 ES:SE:LP:AF:SS:ID   0.0127866:0.0198519:0.284393:0.133956:10817:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0339281    ES:SE:LP:AF:SS:ID   -0.00134495:0.0378596:0.0124848:0.0339281:10817:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.865246 ES:SE:LP:AF:SS:ID   -0.0143277:0.0197787:0.32898:0.865246:10817:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.865336 ES:SE:LP:AF:SS:ID   -0.0138643:0.0197905:0.315516:0.865336:10817:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.865238 ES:SE:LP:AF:SS:ID   -0.0143415:0.0197785:0.32938:0.865238:10817:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00542755   ES:SE:LP:AF:SS:ID   -0.00179029:0.100095:0.00624203:0.00542755:10817:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00539873   ES:SE:LP:AF:SS:ID   0.00154922:0.100307:0.00538462:0.00539873:10817:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00501389   ES:SE:LP:AF:SS:ID   0.035605:0.104066:0.135338:0.00501389:10817:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.865156 ES:SE:LP:AF:SS:ID   -0.0128644:0.0197307:0.288686:0.865156:10817:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.131385 ES:SE:LP:AF:SS:ID   0.0104852:0.0201163:0.220248:0.131385:10817:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.864842 ES:SE:LP:AF:SS:ID   -0.0120072:0.0196938:0.265937:0.864842:10817:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.864068 ES:SE:LP:AF:SS:ID   -0.0126531:0.0196821:0.283729:0.864068:10817:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.864996 ES:SE:LP:AF:SS:ID   -0.012436:0.0197099:0.277296:0.864996:10817:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.865002 ES:SE:LP:AF:SS:ID   -0.0124635:0.0197114:0.278021:0.865002:10817:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.865003 ES:SE:LP:AF:SS:ID   -0.0124491:0.0197112:0.277631:0.865003:10817:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.86538  ES:SE:LP:AF:SS:ID   -0.0138956:0.0197617:0.316976:0.86538:10817:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.1024   ES:SE:LP:AF:SS:ID   0.00824375:0.0230434:0.142343:0.1024:10817:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.0056574    ES:SE:LP:AF:SS:ID   0.0153307:0.0971587:0.0581781:0.0056574:10817:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.869157 ES:SE:LP:AF:SS:ID   -0.0164318:0.0200232:0.385241:0.869157:10817:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.859173 ES:SE:LP:AF:SS:ID   -0.0116997:0.0196954:0.257665:0.859173:10817:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.864055 ES:SE:LP:AF:SS:ID   -0.015585:0.0198479:0.364176:0.864055:10817:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.861162 ES:SE:LP:AF:SS:ID   -0.0126519:0.0198486:0.280781:0.861162:10817:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.103742 ES:SE:LP:AF:SS:ID   0.0304562:0.0236605:0.703232:0.103742:10817:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.86654  ES:SE:LP:AF:SS:ID   -0.0151089:0.0200846:0.34495:0.86654:10817:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.86654  ES:SE:LP:AF:SS:ID   -0.0151071:0.0200846:0.344899:0.86654:10817:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.866556 ES:SE:LP:AF:SS:ID   -0.0153405:0.0200846:0.351632:0.866556:10817:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.866799 ES:SE:LP:AF:SS:ID   -0.0150813:0.0200904:0.344028:0.866799:10817:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.130978 ES:SE:LP:AF:SS:ID   0.0145497:0.0200979:0.328717:0.130978:10817:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.108185 ES:SE:LP:AF:SS:ID   0.00914238:0.0220592:0.168416:0.108185:10817:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.851734 ES:SE:LP:AF:SS:ID   -0.0144517:0.0197951:0.332204:0.851734:10817:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00196822   ES:SE:LP:AF:SS:ID   -0.103069:0.160931:0.282422:0.00196822:10817:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838942 ES:SE:LP:AF:SS:ID   0.00513384:0.0197997:0.0994046:0.838942:10817:rs376645387