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-30120_irnt/ukb-d-30120_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-30120_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 14:52:48 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-30120_irnt/ukb-d-30120_irnt.vcf.gz ...
Read summary statistics for 13586291 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: 0.1711 (0.0111)
Lambda GC: 1.6628
Mean Chi^2: 2.4387
Intercept: 1.1935 (0.0224)
Ratio: 0.1345 (0.0156)
Analysis finished at Mon Nov 25 14:55:29 2019
Total time elapsed: 2.0m:41.01s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.4103,
    "mean_EFFECT": 0.0001,
    "n": 349856,
    "n_snps": 13586291,
    "n_clumped_hits": 343,
    "n_p_sig": 83149,
    "n_mono": 0,
    "n_ns": 1251790,
    "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": 569188,
    "n_est": 351183.1096,
    "ratio_se_n": 1.0019,
    "mean_diff": 9.4751e-07,
    "ratio_diff": 1.109,
    "sd_y_est1": 0.9965,
    "sd_y_est2": 0.9984,
    "r2_sum1": 0.084,
    "r2_sum2": 0.0846,
    "r2_sum3": 0.0842,
    "r2_sum4": 0.0839,
    "ldsc_nsnp_merge_refpanel_ld": 1283504,
    "ldsc_nsnp_merge_regression_ld": 1283504,
    "ldsc_observed_scale_h2_beta": 0.1711,
    "ldsc_observed_scale_h2_se": 0.0111,
    "ldsc_intercept_beta": 1.1935,
    "ldsc_intercept_se": 0.0224,
    "ldsc_lambda_gc": 1.6628,
    "ldsc_mean_chisq": 2.4387,
    "ldsc_ratio": 0.1345
}

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 13574245 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 9.051330e+00 6.184986e+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 7.902511e+07 5.591484e+07 3.02000e+02 3.293100e+07 7.013867e+07 1.148588e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 5.750000e-05 1.505630e-02 -6.20677e-01 -4.590600e-03 2.790000e-05 4.664900e-03 2.240720e-01 ▁▁▁▇▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.001070e-02 9.656700e-03 1.95810e-03 2.850000e-03 5.252000e-03 1.438620e-02 4.901580e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.428736e-01 3.051310e-01 0.00000e+00 1.636839e-01 4.231321e-01 7.073853e-01 1.000000e+00 ▇▅▅▅▅
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.428731e-01 3.051313e-01 0.00000e+00 1.636831e-01 4.231319e-01 7.073858e-01 1.000000e+00 ▇▅▅▅▅
numeric AF 0 1.0000000 NA NA NA NA NA 1.908058e-01 2.544033e-01 1.00000e-03 7.430400e-03 6.003380e-02 2.953175e-01 9.989990e-01 ▇▂▁▁▁
numeric AF_reference 569188 0.9581057 NA NA NA NA NA 1.963354e-01 2.471013e-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 3.498560e+05 0.000000e+00 3.49856e+05 3.498560e+05 3.498560e+05 3.498560e+05 3.498560e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0029528 0.0041709 0.4789663 0.4789655 0.1106480 0.1894970 349856
1 693731 rs12238997 A G 0.0039889 0.0039412 0.3114928 0.3114925 0.1158220 0.1417730 349856
1 707522 rs371890604 G C 0.0006516 0.0044323 0.8831281 0.8831277 0.0972503 0.1293930 349856
1 717587 rs144155419 G A 0.0210176 0.0105612 0.0465843 0.0465825 0.0157180 0.0045926 349856
1 723329 rs189787166 A T -0.0034930 0.0313513 0.9112889 0.9112887 0.0017201 0.0003994 349856
1 730087 rs148120343 T C -0.0031484 0.0054884 0.5662145 0.5662136 0.0564790 0.0127796 349856
1 731718 rs142557973 T C 0.0040497 0.0037376 0.2785877 0.2785882 0.1217450 0.1543530 349856
1 732032 rs61770163 A C 0.0020643 0.0039875 0.6046736 0.6046736 0.1211440 0.1555510 349856
1 734349 rs141242758 T C 0.0039006 0.0037396 0.2969320 0.2969316 0.1209750 0.1525560 349856
1 740284 rs61770167 C T 0.0298106 0.0171082 0.0814273 0.0814255 0.0057856 0.0023962 349856
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0116927 0.0043164 0.0067506 0.0067504 0.0561631 0.0309934 349856
23 154923374 rs111332691 T A -0.0032838 0.0047452 0.4889213 0.4889203 0.0447941 0.0116556 349856
23 154925045 rs509981 C T -0.0130008 0.0022835 0.0000000 0.0000000 0.2454420 0.3634440 349856
23 154925895 rs538470 C T -0.0125250 0.0023357 0.0000001 0.0000001 0.2417880 0.3634440 349856
23 154927581 rs644138 G A -0.0085319 0.0021473 0.0000709 0.0000709 0.3020000 0.4635760 349856
23 154929412 rs557132 C T -0.0130019 0.0022842 0.0000000 0.0000000 0.2452960 0.3568210 349856
23 154929637 rs35185538 CT C -0.0124900 0.0023842 0.0000002 0.0000002 0.2295270 0.3011920 349856
23 154929952 rs4012982 CAA C -0.0135538 0.0024025 0.0000000 0.0000000 0.2392510 0.3165560 349856
23 154930230 rs781880 A G -0.0127902 0.0022838 0.0000000 0.0000000 0.2457070 0.3618540 349856
23 154930487 rs781879 T A -0.0117048 0.0078400 0.1354481 0.1354484 0.0195212 0.1263580 349856

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.110648 ES:SE:LP:AF:SS:ID   -0.00295284:0.00417087:0.319695:0.110648:349856:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.115822 ES:SE:LP:AF:SS:ID   0.00398889:0.00394122:0.506552:0.115822:349856:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0972503    ES:SE:LP:AF:SS:ID   0.000651577:0.00443233:0.0539763:0.0972503:349856:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.015718 ES:SE:LP:AF:SS:ID   0.0210176:0.0105612:1.33176:0.015718:349856:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00172006   ES:SE:LP:AF:SS:ID   -0.00349295:0.0313513:0.0403439:0.00172006:349856:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.056479 ES:SE:LP:AF:SS:ID   -0.00314836:0.00548842:0.247019:0.056479:349856:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121745 ES:SE:LP:AF:SS:ID   0.00404973:0.00373765:0.555038:0.121745:349856:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121144 ES:SE:LP:AF:SS:ID   0.00206431:0.00398753:0.218479:0.121144:349856:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.120975 ES:SE:LP:AF:SS:ID   0.00390059:0.00373965:0.527343:0.120975:349856:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00578559   ES:SE:LP:AF:SS:ID   0.0298106:0.0171082:1.08923:0.00578559:349856:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00188274   ES:SE:LP:AF:SS:ID   -0.0513352:0.0323096:0.950414:0.00188274:349856:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869759 ES:SE:LP:AF:SS:ID   -0.00232401:0.0036871:0.276961:0.869759:349856:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00153176   ES:SE:LP:AF:SS:ID   -0.0617742:0.0333578:1.19351:0.00153176:349856:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.122895 ES:SE:LP:AF:SS:ID   0.00361123:0.00365132:0.491264:0.122895:349856:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142708 ES:SE:LP:AF:SS:ID   0.00419837:0.0036092:0.611307:0.142708:349856:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123012 ES:SE:LP:AF:SS:ID   0.00366296:0.00364621:0.501561:0.123012:349856:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.870892 ES:SE:LP:AF:SS:ID   -0.0043185:0.00355306:0.649361:0.870892:349856:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.875492 ES:SE:LP:AF:SS:ID   -0.00327237:0.00361205:0.437757:0.875492:349856:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128631 ES:SE:LP:AF:SS:ID   0.00466714:0.0035619:0.721025:0.128631:349856:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0363478    ES:SE:LP:AF:SS:ID   -0.00839691:0.00646543:0.712122:0.0363478:349856:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870485 ES:SE:LP:AF:SS:ID   -0.00446467:0.00354931:0.68104:0.870485:349856:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870587 ES:SE:LP:AF:SS:ID   -0.004282:0.00355088:0.642336:0.870587:349856:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870481 ES:SE:LP:AF:SS:ID   -0.00447002:0.00354917:0.682217:0.870481:349856:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00504553   ES:SE:LP:AF:SS:ID   0.0115621:0.0182239:0.279188:0.00504553:349856:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00501236   ES:SE:LP:AF:SS:ID   0.011918:0.0182725:0.288827:0.00501236:349856:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00558059   ES:SE:LP:AF:SS:ID   0.0331685:0.0176572:1.21955:0.00558059:349856:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870565 ES:SE:LP:AF:SS:ID   -0.00410293:0.00354295:0.607579:0.870565:349856:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.125973 ES:SE:LP:AF:SS:ID   0.00397742:0.00361539:0.566593:0.125973:349856:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.870129 ES:SE:LP:AF:SS:ID   -0.00400755:0.00353442:0.590317:0.870129:349856:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869408 ES:SE:LP:AF:SS:ID   -0.00398773:0.00353106:0.587103:0.869408:349856:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870284 ES:SE:LP:AF:SS:ID   -0.00405546:0.00353748:0.599257:0.870284:349856:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.87029  ES:SE:LP:AF:SS:ID   -0.00405205:0.00353773:0.59851:0.87029:349856:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870298 ES:SE:LP:AF:SS:ID   -0.00405147:0.00353784:0.598365:0.870298:349856:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.87074  ES:SE:LP:AF:SS:ID   -0.00403902:0.00354684:0.593797:0.87074:349856:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0987996    ES:SE:LP:AF:SS:ID   0.00140635:0.0041229:0.134882:0.0987996:349856:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00647264   ES:SE:LP:AF:SS:ID   0.0244677:0.0159518:0.902864:0.00647264:349856:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.874649 ES:SE:LP:AF:SS:ID   -0.00347315:0.00359985:0.475417:0.874649:349856:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.864035 ES:SE:LP:AF:SS:ID   -0.0031309:0.00353007:0.425829:0.864035:349856:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.869419 ES:SE:LP:AF:SS:ID   -0.00338157:0.00356436:0.465002:0.869419:349856:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866376 ES:SE:LP:AF:SS:ID   -0.00416098:0.00356517:0.614102:0.866376:349856:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0987131    ES:SE:LP:AF:SS:ID   0.00213746:0.00426965:0.209968:0.0987131:349856:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.87156  ES:SE:LP:AF:SS:ID   -0.00384705:0.00360578:0.543616:0.87156:349856:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.87156  ES:SE:LP:AF:SS:ID   -0.00384646:0.00360578:0.543505:0.87156:349856:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.87156  ES:SE:LP:AF:SS:ID   -0.00384672:0.00360585:0.54354:0.87156:349856:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871932 ES:SE:LP:AF:SS:ID   -0.00377199:0.00360789:0.529:0.871932:349856:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125555 ES:SE:LP:AF:SS:ID   0.00370483:0.00361071:0.515898:0.125555:349856:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105385 ES:SE:LP:AF:SS:ID   0.00012244:0.0039316:0.0109259:0.105385:349856:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855371 ES:SE:LP:AF:SS:ID   -0.00346834:0.00352908:0.487166:0.855371:349856:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00164256   ES:SE:LP:AF:SS:ID   -0.00299561:0.0314057:0.0343233:0.00164256:349856:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838282 ES:SE:LP:AF:SS:ID   -0.00234591:0.00347597:0.301253:0.838282:349856:rs376645387