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-1747_1/ukb-d-1747_1.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-1747_1/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 16:53:39 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-1747_1/ukb-d-1747_1.vcf.gz ...
Read summary statistics for 13586530 SNPs.
Dropped 12711 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: 0.1305 (0.0284)
Lambda GC: 1.2496
Mean Chi^2: 1.9215
Intercept: 1.0881 (0.0148)
Ratio: 0.0956 (0.016)
Analysis finished at Mon Nov 25 16:55:58 2019
Total time elapsed: 2.0m:19.37s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9521,
    "inflation_factor": 1.1596,
    "mean_EFFECT": -0,
    "n": 360270,
    "n_snps": 13586530,
    "n_clumped_hits": 167,
    "n_p_sig": 31253,
    "n_mono": 0,
    "n_ns": 1251807,
    "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": 569284,
    "n_est": 361644.299,
    "ratio_se_n": 1.0019,
    "mean_diff": -0.0001,
    "ratio_diff": 308.3643,
    "sd_y_est1": 0.3185,
    "sd_y_est2": 0.3191,
    "r2_sum1": 0.0081,
    "r2_sum2": 0.0794,
    "r2_sum3": 0.0791,
    "r2_sum4": 0.0766,
    "ldsc_nsnp_merge_refpanel_ld": 1283504,
    "ldsc_nsnp_merge_regression_ld": 1283504,
    "ldsc_observed_scale_h2_beta": 0.1305,
    "ldsc_observed_scale_h2_se": 0.0284,
    "ldsc_intercept_beta": 1.0881,
    "ldsc_intercept_se": 0.0148,
    "ldsc_lambda_gc": 1.2496,
    "ldsc_mean_chisq": 1.9215,
    "ldsc_ratio": 0.0956
}

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 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 13574483 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 33336 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.051332e+00 6.184976e+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 7.902492e+07 5.591479e+07 3.02000e+02 3.293086e+07 7.013828e+07 1.148589e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -2.500000e-05 4.583100e-03 -1.16141e-01 -1.306200e-03 -1.870000e-05 1.253400e-03 1.550290e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 3.152900e-03 3.041400e-03 6.17000e-04 8.976000e-04 1.654100e-03 4.531100e-03 1.529850e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.747661e-01 2.971785e-01 0.00000e+00 2.112511e-01 4.676339e-01 7.327469e-01 1.000000e+00 ▇▆▆▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.747656e-01 2.971787e-01 0.00000e+00 2.112499e-01 4.676333e-01 7.327463e-01 1.000000e+00 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA 1.908023e-01 2.544021e-01 1.00000e-03 7.429300e-03 6.003020e-02 2.953080e-01 9.989990e-01 ▇▂▁▁▁
numeric AF_reference 569284 0.9580994 NA NA NA NA NA 1.963336e-01 2.471006e-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 3.602700e+05 0.000000e+00 3.60270e+05 3.602700e+05 3.602700e+05 3.602700e+05 3.602700e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0004502 0.0013138 0.7318566 0.7318553 0.1106500 0.1894970 360270
1 693731 rs12238997 A G 0.0002565 0.0012415 0.8363389 0.8363397 0.1158380 0.1417730 360270
1 707522 rs371890604 G C 0.0002504 0.0013958 0.8576390 0.8576392 0.0972910 0.1293930 360270
1 717587 rs144155419 G A 0.0010301 0.0033290 0.7570002 0.7570001 0.0157019 0.0045926 360270
1 723329 rs189787166 A T -0.0026846 0.0098362 0.7849048 0.7849034 0.0017308 0.0003994 360270
1 730087 rs148120343 T C 0.0002153 0.0017298 0.9009600 0.9009602 0.0564472 0.0127796 360270
1 731718 rs142557973 T C 0.0002549 0.0011774 0.8286089 0.8286081 0.1217410 0.1543530 360270
1 732032 rs61770163 A C 0.0002382 0.0012560 0.8495620 0.8495620 0.1211730 0.1555510 360270
1 734349 rs141242758 T C 0.0002999 0.0011780 0.7990199 0.7990202 0.1209690 0.1525560 360270
1 740284 rs61770167 C T -0.0104957 0.0053871 0.0513771 0.0513771 0.0057907 0.0023962 360270
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0028714 0.0013609 0.0348698 0.0348692 0.0561460 0.0309934 360270
23 154923374 rs111332691 T A 0.0020543 0.0014954 0.1695028 0.1695029 0.0447845 0.0116556 360270
23 154925045 rs509981 C T 0.0001555 0.0007195 0.8289249 0.8289247 0.2456300 0.3634440 360270
23 154925895 rs538470 C T 0.0001367 0.0007360 0.8526480 0.8526473 0.2419380 0.3634440 360270
23 154927581 rs644138 G A -0.0005422 0.0006766 0.4228974 0.4228957 0.3021610 0.4635760 360270
23 154929412 rs557132 C T 0.0001697 0.0007197 0.8136220 0.8136213 0.2454820 0.3568210 360270
23 154929637 rs35185538 CT C 0.0007974 0.0007511 0.2883640 0.2883633 0.2297100 0.3011920 360270
23 154929952 rs4012982 CAA C 0.0002604 0.0007569 0.7308529 0.7308530 0.2394580 0.3165560 360270
23 154930230 rs781880 A G 0.0001656 0.0007195 0.8180130 0.8180131 0.2458930 0.3618540 360270
23 154930487 rs781879 T A 0.0026043 0.0024673 0.2911937 0.2911923 0.0195732 0.1263580 360270

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.11065  ES:SE:LP:AF:SS:ID   0.000450202:0.00131385:0.135574:0.11065:360270:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.115838 ES:SE:LP:AF:SS:ID   0.00025646:0.00124147:0.0776177:0.115838:360270:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.097291 ES:SE:LP:AF:SS:ID   0.000250386:0.00139584:0.0666955:0.097291:360270:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157019    ES:SE:LP:AF:SS:ID   0.00103008:0.00332904:0.120904:0.0157019:360270:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00173076   ES:SE:LP:AF:SS:ID   -0.00268464:0.0098362:0.105183:0.00173076:360270:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0564472    ES:SE:LP:AF:SS:ID   0.000215266:0.00172976:0.0452945:0.0564472:360270:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121741 ES:SE:LP:AF:SS:ID   0.000254894:0.00117741:0.0816504:0.121741:360270:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121173 ES:SE:LP:AF:SS:ID   0.000238229:0.00125597:0.0708049:0.121173:360270:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.120969 ES:SE:LP:AF:SS:ID   0.000299947:0.00117804:0.0974424:0.120969:360270:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00579066   ES:SE:LP:AF:SS:ID   -0.0104957:0.00538706:1.28923:0.00579066:360270:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00187672   ES:SE:LP:AF:SS:ID   0.0128994:0.0101832:0.68771:0.00187672:360270:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869737 ES:SE:LP:AF:SS:ID   0.000354388:0.00116136:0.119042:0.869737:360270:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.00153102   ES:SE:LP:AF:SS:ID   0.0111823:0.0105115:0.541492:0.00153102:360270:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.122922 ES:SE:LP:AF:SS:ID   -0.000359262:0.00115019:0.122183:0.122922:360270:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142717 ES:SE:LP:AF:SS:ID   -0.000589326:0.00113688:0.218819:0.142717:360270:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123037 ES:SE:LP:AF:SS:ID   -0.000348142:0.00114859:0.118153:0.123037:360270:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.87088  ES:SE:LP:AF:SS:ID   0.000107717:0.00111926:0.0346426:0.87088:360270:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.875466 ES:SE:LP:AF:SS:ID   0.000127693:0.00113783:0.0406509:0.875466:360270:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128643 ES:SE:LP:AF:SS:ID   -7.91592e-05:0.00112205:0.0251398:0.128643:360270:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0363464    ES:SE:LP:AF:SS:ID   -0.00288117:0.00203609:0.803948:0.0363464:360270:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870474 ES:SE:LP:AF:SS:ID   -1.15102e-06:0.0011181:0.000356702:0.870474:360270:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870579 ES:SE:LP:AF:SS:ID   7.00293e-06:0.00111861:0.00217474:0.870579:360270:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870471 ES:SE:LP:AF:SS:ID   2.10472e-05:0.00111805:0.00657215:0.870471:360270:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00505041   ES:SE:LP:AF:SS:ID   -0.00826653:0.00573598:0.825251:0.00505041:360270:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00501736   ES:SE:LP:AF:SS:ID   -0.00843823:0.00575132:0.84671:0.00501736:360270:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00558719   ES:SE:LP:AF:SS:ID   -0.00483492:0.00555873:0.415197:0.00558719:360270:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870552 ES:SE:LP:AF:SS:ID   0.000193375:0.00111608:0.0642686:0.870552:360270:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.125993 ES:SE:LP:AF:SS:ID   -0.000293013:0.00113881:0.0985689:0.125993:360270:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.870117 ES:SE:LP:AF:SS:ID   0.000206021:0.0011134:0.0689497:0.870117:360270:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869406 ES:SE:LP:AF:SS:ID   0.000134639:0.00111238:0.043994:0.869406:360270:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870272 ES:SE:LP:AF:SS:ID   0.000183199:0.00111436:0.0607719:0.870272:360270:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870278 ES:SE:LP:AF:SS:ID   0.000181929:0.00111444:0.0603195:0.870278:360270:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870287 ES:SE:LP:AF:SS:ID   0.000179804:0.00111447:0.0595687:0.870287:360270:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870728 ES:SE:LP:AF:SS:ID   0.000166236:0.00111731:0.0546668:0.870728:360270:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0988323    ES:SE:LP:AF:SS:ID   -0.000208817:0.00129847:0.0593655:0.0988323:360270:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00647755   ES:SE:LP:AF:SS:ID   -0.00610413:0.00502175:0.64944:0.00647755:360270:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.874628 ES:SE:LP:AF:SS:ID   0.000140998:0.00113398:0.0452526:0.874628:360270:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.864021 ES:SE:LP:AF:SS:ID   0.000344748:0.00111202:0.121165:0.864021:360270:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.869415 ES:SE:LP:AF:SS:ID   0.000150047:0.00112281:0.0488126:0.869415:360270:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866359 ES:SE:LP:AF:SS:ID   0.00013012:0.00112304:0.042029:0.866359:360270:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0987912    ES:SE:LP:AF:SS:ID   -0.00123351:0.00134448:0.445024:0.0987912:360270:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.871548 ES:SE:LP:AF:SS:ID   0.000217443:0.00113585:0.0715099:0.871548:360270:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.871548 ES:SE:LP:AF:SS:ID   0.000217602:0.00113585:0.0715663:0.871548:360270:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.871547 ES:SE:LP:AF:SS:ID   0.000216376:0.00113587:0.0711322:0.871547:360270:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871922 ES:SE:LP:AF:SS:ID   0.000189829:0.00113652:0.0618061:0.871922:360270:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.12557  ES:SE:LP:AF:SS:ID   -0.000258063:0.0011374:0.0859156:0.12557:360270:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105413 ES:SE:LP:AF:SS:ID   -0.000282974:0.00123831:0.0865857:0.105413:360270:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855359 ES:SE:LP:AF:SS:ID   0.000377944:0.00111159:0.13439:0.855359:360270:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00165156   ES:SE:LP:AF:SS:ID   -0.00335191:0.00985742:0.134406:0.00165156:360270:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838382 ES:SE:LP:AF:SS:ID   0.00174772:0.00109534:0.956316:0.838382:360270:rs376645387