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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    "FORMAT.1": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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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-22608_2/ukb-d-22608_2.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-22608_2/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 16:36:30 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22608_2/ukb-d-22608_2.vcf.gz ...
Read summary statistics for 13296637 SNPs.
Dropped 12320 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, 1283449 SNPs remain.
After merging with regression SNP LD, 1283449 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0274 (0.0049)
Lambda GC: 1.0466
Mean Chi^2: 1.0454
Intercept: 0.9975 (0.006)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 16:38:44 2019
Total time elapsed: 2.0m:13.9s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9516,
    "inflation_factor": 1.0303,
    "mean_EFFECT": 0,
    "n": 90105,
    "n_snps": 13296637,
    "n_clumped_hits": 0,
    "n_p_sig": 1,
    "n_mono": 0,
    "n_ns": 1241550,
    "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": 494955,
    "n_est": 90474.6848,
    "ratio_se_n": 1.002,
    "mean_diff": 0.0001,
    "ratio_diff": 18.2535,
    "sd_y_est1": 0.3156,
    "sd_y_est2": 0.3163,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1283449,
    "ldsc_nsnp_merge_regression_ld": 1283449,
    "ldsc_observed_scale_h2_beta": 0.0274,
    "ldsc_observed_scale_h2_se": 0.0049,
    "ldsc_intercept_beta": 0.9975,
    "ldsc_intercept_se": 0.006,
    "ldsc_lambda_gc": 1.0466,
    "ldsc_mean_chisq": 1.0454,
    "ldsc_ratio": -0.0551
}

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 13284975 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 56494 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 33177 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.055077e+00 6.185614e+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 7.900127e+07 5.592530e+07 3.02000e+02 3.288589e+07 7.009652e+07 1.148380e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 2.360000e-05 8.096500e-03 -8.77006e-02 -2.273800e-03 1.400000e-06 2.265200e-03 1.145910e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 5.878000e-03 5.534400e-03 1.22710e-03 1.765900e-03 3.160500e-03 8.395200e-03 2.999310e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.954380e-01 2.898436e-01 0.00000e+00 2.437132e-01 4.935657e-01 7.463199e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.954362e-01 2.898446e-01 0.00000e+00 2.437102e-01 4.935636e-01 7.463191e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 1.947326e-01 2.553546e-01 1.22920e-03 8.476100e-03 6.498530e-02 3.031190e-01 9.987710e-01 ▇▂▁▁▁
numeric AF_reference 494955 0.9627759 NA NA NA NA NA 1.992845e-01 2.477487e-01 0.00000e+00 6.389800e-03 8.825880e-02 3.136980e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 9.010500e+04 0.000000e+00 9.01050e+04 9.010500e+04 9.010500e+04 9.010500e+04 9.010500e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C -0.0020923 0.0025985 0.4207063 0.4207053 0.1113430 0.1894970 90105
1 693731 rs12238997 A G -0.0011155 0.0024607 0.6503213 0.6503191 0.1163310 0.1417730 90105
1 707522 rs371890604 G C -0.0001755 0.0027658 0.9494150 0.9494146 0.0977618 0.1293930 90105
1 717587 rs144155419 G A -0.0019380 0.0065905 0.7687126 0.7687119 0.0157787 0.0045926 90105
1 723329 rs189787166 A T 0.0008735 0.0192755 0.9638550 0.9638546 0.0017681 0.0003994 90105
1 730087 rs148120343 T C -0.0006122 0.0034137 0.8576630 0.8576623 0.0568517 0.0127796 90105
1 731718 rs142557973 T C -0.0009404 0.0023352 0.6871586 0.6871567 0.1222030 0.1543530 90105
1 732032 rs61770163 A C -0.0009295 0.0024898 0.7089017 0.7089005 0.1219220 0.1555510 90105
1 734349 rs141242758 T C -0.0008313 0.0023370 0.7220462 0.7220450 0.1214330 0.1525560 90105
1 740284 rs61770167 C T 0.0035399 0.0107056 0.7409024 0.7409023 0.0057643 0.0023962 90105
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0050814 0.0027313 0.0628217 0.0628184 0.0552866 0.0309934 90105
23 154923374 rs111332691 T A -0.0046191 0.0029695 0.1198221 0.1198190 0.0449254 0.0116556 90105
23 154925045 rs509981 C T 0.0013963 0.0014319 0.3294769 0.3294746 0.2445960 0.3634440 90105
23 154925895 rs538470 C T 0.0010548 0.0014639 0.4711975 0.4711967 0.2410980 0.3634440 90105
23 154927581 rs644138 G A 0.0000727 0.0013469 0.9569439 0.9569439 0.3002880 0.4635760 90105
23 154929412 rs557132 C T 0.0013579 0.0014323 0.3430986 0.3430939 0.2444530 0.3568210 90105
23 154929637 rs35185538 CT C 0.0012385 0.0014956 0.4076224 0.4076207 0.2288420 0.3011920 90105
23 154929952 rs4012982 CAA C 0.0009909 0.0015077 0.5110225 0.5110205 0.2383240 0.3165560 90105
23 154930230 rs781880 A G 0.0013478 0.0014318 0.3465461 0.3465446 0.2449110 0.3618540 90105
23 154930487 rs781879 T A 0.0003836 0.0048579 0.9370611 0.9370614 0.0196486 0.1263580 90105

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111343 ES:SE:LP:AF:SS:ID   -0.00209234:0.00259854:0.376021:0.111343:90105:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116331 ES:SE:LP:AF:SS:ID   -0.0011155:0.00246074:0.186872:0.116331:90105:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0977618    ES:SE:LP:AF:SS:ID   -0.000175467:0.00276579:0.0225439:0.0977618:90105:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0157787    ES:SE:LP:AF:SS:ID   -0.00193801:0.00659052:0.114236:0.0157787:90105:rs144155419
1   723329  rs189787166 A   T   .   PASS    AF=0.00176813   ES:SE:LP:AF:SS:ID   0.000873509:0.0192755:0.0159883:0.00176813:90105:rs189787166
1   730087  rs148120343 T   C   .   PASS    AF=0.0568517    ES:SE:LP:AF:SS:ID   -0.000612248:0.00341369:0.0666833:0.0568517:90105:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122203 ES:SE:LP:AF:SS:ID   -0.000940428:0.00233521:0.162943:0.122203:90105:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121922 ES:SE:LP:AF:SS:ID   -0.000929521:0.00248979:0.149414:0.121922:90105:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121433 ES:SE:LP:AF:SS:ID   -0.000831334:0.002337:0.141435:0.121433:90105:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.00576432   ES:SE:LP:AF:SS:ID   0.0035399:0.0107056:0.130239:0.00576432:90105:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.00181475   ES:SE:LP:AF:SS:ID   -0.0129847:0.0205514:0.277771:0.00181475:90105:rs112573343
1   749963  rs529266287 T   TAA .   PASS    AF=0.869063 ES:SE:LP:AF:SS:ID   0.000652854:0.00229891:0.109901:0.869063:90105:rs529266287
1   750230  rs190826124 G   C   .   PASS    AF=0.0015992    ES:SE:LP:AF:SS:ID   -8.49713e-05:0.0203204:0.00145123:0.0015992:90105:rs190826124
1   751343  rs28544273  T   A   .   PASS    AF=0.123555 ES:SE:LP:AF:SS:ID   -0.000757196:0.00227911:0.130936:0.123555:90105:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.14307  ES:SE:LP:AF:SS:ID   -0.000812566:0.00225611:0.143436:0.14307:90105:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123692 ES:SE:LP:AF:SS:ID   -0.000790902:0.00227581:0.137751:0.123692:90105:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.87006  ES:SE:LP:AF:SS:ID   0.000855149:0.00221759:0.15504:0.87006:90105:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.874709 ES:SE:LP:AF:SS:ID   0.00104851:0.00225364:0.192633:0.874709:90105:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.12937  ES:SE:LP:AF:SS:ID   -0.000647096:0.00222448:0.112872:0.12937:90105:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0358637    ES:SE:LP:AF:SS:ID   0.00210925:0.00406054:0.219359:0.0358637:90105:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869707 ES:SE:LP:AF:SS:ID   0.000927227:0.00221574:0.170308:0.869707:90105:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869785 ES:SE:LP:AF:SS:ID   0.000899908:0.00221672:0.164455:0.869785:90105:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869704 ES:SE:LP:AF:SS:ID   0.000922579:0.00221565:0.169332:0.869704:90105:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00533145   ES:SE:LP:AF:SS:ID   -0.00392883:0.0110446:0.141434:0.00533145:90105:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00529672   ES:SE:LP:AF:SS:ID   -0.00472265:0.011072:0.17411:0.00529672:90105:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.00566082   ES:SE:LP:AF:SS:ID   0.0201665:0.0109028:1.19134:0.00566082:90105:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.869689 ES:SE:LP:AF:SS:ID   0.000863056:0.00221112:0.157205:0.869689:90105:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126763 ES:SE:LP:AF:SS:ID   -0.000750663:0.00225698:0.131097:0.126763:90105:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.869214 ES:SE:LP:AF:SS:ID   0.000869827:0.00220536:0.159094:0.869214:90105:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868604 ES:SE:LP:AF:SS:ID   0.000965725:0.00220394:0.17963:0.868604:90105:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869367 ES:SE:LP:AF:SS:ID   0.000843352:0.00220738:0.153405:0.869367:90105:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869373 ES:SE:LP:AF:SS:ID   0.000839116:0.00220752:0.152514:0.869373:90105:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869375 ES:SE:LP:AF:SS:ID   0.000840614:0.00220754:0.152824:0.869375:90105:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869858 ES:SE:LP:AF:SS:ID   0.000876098:0.00221347:0.159736:0.869858:90105:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0990209    ES:SE:LP:AF:SS:ID   0.000743981:0.00257642:0.111955:0.0990209:90105:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00649969   ES:SE:LP:AF:SS:ID   0.0140668:0.00993317:0.804826:0.00649969:90105:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.873849 ES:SE:LP:AF:SS:ID   0.00112747:0.00224628:0.210617:0.873849:90105:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.86323  ES:SE:LP:AF:SS:ID   0.000441019:0.00220336:0.0750192:0.86323:90105:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868645 ES:SE:LP:AF:SS:ID   0.00113261:0.00222518:0.214132:0.868645:90105:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.865535 ES:SE:LP:AF:SS:ID   0.0013212:0.00222547:0.257485:0.865535:90105:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0994347    ES:SE:LP:AF:SS:ID   -0.000539912:0.00266329:0.0760559:0.0994347:90105:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870818 ES:SE:LP:AF:SS:ID   0.00105313:0.00225097:0.193895:0.870818:90105:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870818 ES:SE:LP:AF:SS:ID   0.00105296:0.00225097:0.193859:0.870818:90105:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870815 ES:SE:LP:AF:SS:ID   0.0010541:0.00225098:0.194105:0.870815:90105:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871183 ES:SE:LP:AF:SS:ID   0.00117234:0.00225204:0.219921:0.871183:90105:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126356 ES:SE:LP:AF:SS:ID   -0.000747413:0.0022545:0.13062:0.126356:90105:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.10589  ES:SE:LP:AF:SS:ID   2.97018e-05:0.00245099:0.00421963:0.10589:90105:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854591 ES:SE:LP:AF:SS:ID   0.00110771:0.0022028:0.211083:0.854591:90105:rs2519015
1   767393  rs538667473 A   C   .   PASS    AF=0.00167824   ES:SE:LP:AF:SS:ID   0.0022937:0.0193437:0.0430578:0.00167824:90105:rs538667473
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839278 ES:SE:LP:AF:SS:ID   0.0029113:0.00217603:0.742477:0.839278:90105:rs376645387