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-20116_0/ukb-d-20116_0.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-20116_0/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 16:36:48 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-20116_0/ukb-d-20116_0.vcf.gz ...
Read summary statistics for 13586590 SNPs.
Dropped 12712 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: 0.0946 (0.0033)
Lambda GC: 1.5284
Mean Chi^2: 1.7201
Intercept: 1.043 (0.0093)
Ratio: 0.0597 (0.0129)
Analysis finished at Mon Nov 25 16:38:56 2019
Total time elapsed: 2.0m:7.75s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.3199,
    "mean_EFFECT": -0,
    "n": 359706,
    "n_snps": 13586590,
    "n_clumped_hits": 84,
    "n_p_sig": 7223,
    "n_mono": 0,
    "n_ns": 1251798,
    "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": 569311,
    "n_est": 361098.2781,
    "ratio_se_n": 1.0019,
    "mean_diff": -0,
    "ratio_diff": 36.4994,
    "sd_y_est1": 0.4942,
    "sd_y_est2": 0.4951,
    "r2_sum1": 0.0023,
    "r2_sum2": 0.0095,
    "r2_sum3": 0.0095,
    "r2_sum4": 0.0095,
    "ldsc_nsnp_merge_refpanel_ld": 1283504,
    "ldsc_nsnp_merge_regression_ld": 1283504,
    "ldsc_observed_scale_h2_beta": 0.0946,
    "ldsc_observed_scale_h2_se": 0.0033,
    "ldsc_intercept_beta": 1.043,
    "ldsc_intercept_se": 0.0093,
    "ldsc_lambda_gc": 1.5284,
    "ldsc_mean_chisq": 1.7201,
    "ldsc_ratio": 0.0597
}

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 13574542 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 57082 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 9.051342e+00 6.184980e+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 7.902504e+07 5.591476e+07 3.02000e+02 3.293099e+07 7.013861e+07 1.148589e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -4.030000e-05 7.059300e-03 -9.54793e-02 -2.190300e-03 -2.790000e-05 2.125700e-03 8.806780e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 4.896500e-03 4.723400e-03 9.58000e-04 1.394000e-03 2.568900e-03 7.036800e-03 2.375010e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.543790e-01 3.015124e-01 0.00000e+00 1.815002e-01 4.384025e-01 7.156209e-01 1.000000e+00 ▇▆▆▅▅
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.543786e-01 3.015127e-01 0.00000e+00 1.814984e-01 4.384008e-01 7.156209e-01 9.999997e-01 ▇▆▆▅▅
numeric AF 0 1.0000000 NA NA NA NA NA 1.908016e-01 2.544021e-01 1.00000e-03 7.429000e-03 6.003100e-02 2.953080e-01 9.990000e-01 ▇▂▁▁▁
numeric AF_reference 569311 0.9580976 NA NA NA NA NA 1.963332e-01 2.471007e-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 3.597060e+05 0.000000e+00 3.59706e+05 3.597060e+05 3.597060e+05 3.597060e+05 3.597060e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0001698 0.0020406 0.9336670 0.9336671 0.1106490 0.1894970 359706
1 693731 rs12238997 A G -0.0004412 0.0019282 0.8189909 0.8189908 0.1158130 0.1417730 359706
1 707522 rs371890604 G C 0.0006957 0.0021674 0.7482229 0.7482234 0.0973191 0.1293930 359706
1 717587 rs144155419 G A -0.0042999 0.0051751 0.4060448 0.4060442 0.0156734 0.0045926 359706
1 723329 rs189787166 A T -0.0041224 0.0152510 0.7869280 0.7869277 0.0017355 0.0003994 359706
1 730087 rs148120343 T C 0.0011620 0.0026864 0.6653314 0.6653311 0.0564527 0.0127796 359706
1 731718 rs142557973 T C -0.0012725 0.0018285 0.4864912 0.4864915 0.1217330 0.1543530 359706
1 732032 rs61770163 A C -0.0004653 0.0019504 0.8114319 0.8114319 0.1211840 0.1555510 359706
1 734349 rs141242758 T C -0.0011426 0.0018295 0.5322859 0.5322859 0.1209620 0.1525560 359706
1 740284 rs61770167 C T -0.0137531 0.0083673 0.1002439 0.1002435 0.0057883 0.0023962 359706
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0021378 0.0021131 0.3116772 0.3116754 0.0561412 0.0309934 359706
23 154923374 rs111332691 T A 0.0034746 0.0023209 0.1343739 0.1343728 0.0448033 0.0116556 359706
23 154925045 rs509981 C T -0.0018231 0.0011170 0.1026609 0.1026600 0.2456480 0.3634440 359706
23 154925895 rs538470 C T -0.0016483 0.0011427 0.1491729 0.1491716 0.2419500 0.3634440 359706
23 154927581 rs644138 G A -0.0010568 0.0010505 0.3144452 0.3144445 0.3021760 0.4635760 359706
23 154929412 rs557132 C T -0.0018468 0.0011173 0.0983626 0.0983606 0.2455020 0.3568210 359706
23 154929637 rs35185538 CT C -0.0016594 0.0011661 0.1547359 0.1547362 0.2297500 0.3011920 359706
23 154929952 rs4012982 CAA C -0.0017242 0.0011752 0.1423371 0.1423354 0.2394600 0.3165560 359706
23 154930230 rs781880 A G -0.0018113 0.0011172 0.1049470 0.1049459 0.2459090 0.3618540 359706
23 154930487 rs781879 T A 0.0046985 0.0038328 0.2202450 0.2202447 0.0195604 0.1263580 359706

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.110649 ES:SE:LP:AF:SS:ID   -0.000169839:0.00204055:0.029808:0.110649:359706:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.115813 ES:SE:LP:AF:SS:ID   -0.000441244:0.00192815:0.0867209:0.115813:359706:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0973191    ES:SE:LP:AF:SS:ID   0.000695711:0.00216744:0.125969:0.0973191:359706:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0156734    ES:SE:LP:AF:SS:ID   -0.00429987:0.00517511:0.391426:0.0156734:359706:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.0017355    ES:SE:LP:AF:SS:ID   -0.00412238:0.015251:0.104065:0.0017355:359706:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0564527    ES:SE:LP:AF:SS:ID   0.00116202:0.00268635:0.176962:0.0564527:359706:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121733 ES:SE:LP:AF:SS:ID   -0.00127246:0.00182851:0.312925:0.121733:359706:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121184 ES:SE:LP:AF:SS:ID   -0.000465332:0.00195043:0.0907479:0.121184:359706:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.120962 ES:SE:LP:AF:SS:ID   -0.00114256:0.0018295:0.273855:0.120962:359706:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.0057883    ES:SE:LP:AF:SS:ID   -0.0137531:0.00836729:0.998942:0.0057883:359706:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00188124   ES:SE:LP:AF:SS:ID   0.029591:0.0157861:1.21565:0.00188124:359706:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869737 ES:SE:LP:AF:SS:ID   0.0010811:0.00180355:0.260516:0.869737:359706:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00152752   ES:SE:LP:AF:SS:ID   0.0117007:0.0163474:0.324092:0.00152752:359706:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.122927 ES:SE:LP:AF:SS:ID   -0.00152392:0.00178622:0.404972:0.122927:359706:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142724 ES:SE:LP:AF:SS:ID   -0.00333488:0.00176554:1.22981:0.142724:359706:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123042 ES:SE:LP:AF:SS:ID   -0.00153742:0.00178374:0.410345:0.123042:359706:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.870884 ES:SE:LP:AF:SS:ID   0.00152466:0.00173822:0.419748:0.870884:359706:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.875467 ES:SE:LP:AF:SS:ID   0.00110378:0.00176702:0.273927:0.875467:359706:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128637 ES:SE:LP:AF:SS:ID   -0.00204211:0.00174254:0.617569:0.128637:359706:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0363769    ES:SE:LP:AF:SS:ID   0.000440905:0.00316091:0.051066:0.0363769:359706:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870477 ES:SE:LP:AF:SS:ID   0.00162574:0.00173638:0.457012:0.870477:359706:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870583 ES:SE:LP:AF:SS:ID   0.00160084:0.00173718:0.447597:0.870583:359706:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870474 ES:SE:LP:AF:SS:ID   0.00161976:0.00173632:0.45483:0.870474:359706:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00504716   ES:SE:LP:AF:SS:ID   0.00222253:0.00891087:0.0952644:0.00504716:359706:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00501408   ES:SE:LP:AF:SS:ID   0.00281115:0.00893471:0.123181:0.00501408:359706:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00558496   ES:SE:LP:AF:SS:ID   0.00943532:0.008636:0.561317:0.00558496:359706:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870556 ES:SE:LP:AF:SS:ID   0.00149775:0.00173326:0.411705:0.870556:359706:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.125989 ES:SE:LP:AF:SS:ID   -0.00183676:0.00176858:0.524311:0.125989:359706:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.87012  ES:SE:LP:AF:SS:ID   0.00148605:0.00172909:0.408825:0.87012:359706:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869407 ES:SE:LP:AF:SS:ID   0.00157648:0.0017275:0.441936:0.869407:359706:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870273 ES:SE:LP:AF:SS:ID   0.0014724:0.00173057:0.403544:0.870273:359706:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870279 ES:SE:LP:AF:SS:ID   0.00147361:0.00173069:0.403933:0.870279:359706:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870287 ES:SE:LP:AF:SS:ID   0.00147556:0.00173075:0.404605:0.870287:359706:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870732 ES:SE:LP:AF:SS:ID   0.00148618:0.00173517:0.407028:0.870732:359706:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0988569    ES:SE:LP:AF:SS:ID   -0.000979209:0.00201634:0.202577:0.0988569:359706:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00647497   ES:SE:LP:AF:SS:ID   0.013641:0.0078007:1.09504:0.00647497:359706:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.874633 ES:SE:LP:AF:SS:ID   0.00104548:0.00176107:0.25748:0.874633:359706:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.864019 ES:SE:LP:AF:SS:ID   0.00141984:0.00172692:0.386185:0.864019:359706:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.86942  ES:SE:LP:AF:SS:ID   0.00142569:0.00174372:0.383443:0.86942:359706:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866367 ES:SE:LP:AF:SS:ID   0.00143766:0.00174407:0.387466:0.866367:359706:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0987804    ES:SE:LP:AF:SS:ID   -0.00155147:0.00208816:0.339617:0.0987804:359706:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.871555 ES:SE:LP:AF:SS:ID   0.00128041:0.00176398:0.329824:0.871555:359706:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.871555 ES:SE:LP:AF:SS:ID   0.00128053:0.00176398:0.329863:0.871555:359706:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.871554 ES:SE:LP:AF:SS:ID   0.00127532:0.00176402:0.328176:0.871554:359706:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871928 ES:SE:LP:AF:SS:ID   0.0012866:0.00176503:0.331578:0.871928:359706:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125566 ES:SE:LP:AF:SS:ID   -0.00152627:0.00176639:0.411667:0.125566:359706:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.10544  ES:SE:LP:AF:SS:ID   -0.00138405:0.00192288:0.32637:0.10544:359706:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855376 ES:SE:LP:AF:SS:ID   0.00159201:0.00172636:0.448017:0.855376:359706:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00165652   ES:SE:LP:AF:SS:ID   -0.00467502:0.0152837:0.119361:0.00165652:359706:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838342 ES:SE:LP:AF:SS:ID   0.00303744:0.00170083:1.13005:0.838342:359706:rs376645387