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-20002_1278/ukb-d-20002_1278.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-20002_1278/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 14:50:46 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-20002_1278/ukb-d-20002_1278.vcf.gz ...
Read summary statistics for 12055989 SNPs.
Dropped 10557 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, 1283051 SNPs remain.
After merging with regression SNP LD, 1283051 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0036 (0.0013)
Lambda GC: 1.0104
Mean Chi^2: 1.022
Intercept: 0.9969 (0.0058)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 14:52:34 2019
Total time elapsed: 1.0m:47.69s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9491,
    "inflation_factor": 1.0065,
    "mean_EFFECT": 2.7137e-06,
    "n": 361141,
    "n_snps": 12055989,
    "n_clumped_hits": 2,
    "n_p_sig": 12,
    "n_mono": 0,
    "n_ns": 1187004,
    "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": 289198,
    "n_est": 362019.6367,
    "ratio_se_n": 1.0012,
    "mean_diff": 0,
    "ratio_diff": 35.3233,
    "sd_y_est1": 0.1173,
    "sd_y_est2": 0.1174,
    "r2_sum1": 3.0675e-06,
    "r2_sum2": 0.0002,
    "r2_sum3": 0.0002,
    "r2_sum4": 0.0002,
    "ldsc_nsnp_merge_refpanel_ld": 1283051,
    "ldsc_nsnp_merge_regression_ld": 1283051,
    "ldsc_observed_scale_h2_beta": 0.0036,
    "ldsc_observed_scale_h2_se": 0.0013,
    "ldsc_intercept_beta": 0.9969,
    "ldsc_intercept_se": 0.0058,
    "ldsc_lambda_gc": 1.0104,
    "ldsc_mean_chisq": 1.022,
    "ldsc_ratio": -0.1409
}

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 12046056 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 53525 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 32206 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.068076e+00 6.184809e+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 7.890397e+07 5.597697e+07 3.02000e+02 3.270999e+07 6.993615e+07 1.147682e+08 2.492309e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA 2.700000e-06 1.099600e-03 -1.14972e-02 -3.739000e-04 -2.400000e-06 3.672000e-04 1.401070e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 8.440000e-04 7.021000e-04 2.27000e-04 3.177000e-04 5.065000e-04 1.165600e-03 4.119500e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.985414e-01 2.894429e-01 0.00000e+00 2.473637e-01 4.986077e-01 7.492073e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.985410e-01 2.894432e-01 0.00000e+00 2.473634e-01 4.986080e-01 7.492071e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.137297e-01 2.592264e-01 2.47770e-03 1.497180e-02 9.108150e-02 3.396030e-01 9.975220e-01 ▇▂▁▁▁
numeric AF_reference 289198 0.9760121 NA NA NA NA NA 2.151881e-01 2.507457e-01 0.00000e+00 1.158150e-02 1.108230e-01 3.420530e-01 1.000000e+00 ▇▂▁▁▁
numeric N 0 1.0000000 NA NA NA NA NA 3.611410e+05 0.000000e+00 3.61141e+05 3.611410e+05 3.611410e+05 3.611410e+05 3.611410e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0000805 0.0004836 0.8677400 0.8677395 0.1106370 0.1894970 361141
1 693731 rs12238997 A G 0.0001852 0.0004569 0.6852405 0.6852403 0.1158230 0.1417730 361141
1 707522 rs371890604 G C -0.0003865 0.0005137 0.4518383 0.4518382 0.0972984 0.1293930 361141
1 717587 rs144155419 G A 0.0015133 0.0012258 0.2170091 0.2170076 0.0156864 0.0045926 361141
1 730087 rs148120343 T C 0.0002765 0.0006366 0.6639846 0.6639838 0.0564574 0.0127796 361141
1 731718 rs142557973 T C 0.0000159 0.0004334 0.9707339 0.9707343 0.1217310 0.1543530 361141
1 732032 rs61770163 A C 0.0000974 0.0004623 0.8330510 0.8330503 0.1211620 0.1555510 361141
1 734349 rs141242758 T C 0.0000138 0.0004336 0.9746370 0.9746370 0.1209580 0.1525560 361141
1 740284 rs61770167 C T -0.0005788 0.0019838 0.7704811 0.7704818 0.0057837 0.0023962 361141
1 749963 rs529266287 T TAA 0.0000555 0.0004275 0.8967651 0.8967650 0.8697500 0.7641770 361141
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0003622 0.0005007 0.4694204 0.4694190 0.0561627 0.0309934 361141
23 154923374 rs111332691 T A 0.0002750 0.0005502 0.6171532 0.6171525 0.0447914 0.0116556 361141
23 154925045 rs509981 C T -0.0005292 0.0002647 0.0456205 0.0456195 0.2455960 0.3634440 361141
23 154925895 rs538470 C T -0.0005536 0.0002708 0.0409157 0.0409148 0.2419030 0.3634440 361141
23 154927581 rs644138 G A -0.0005577 0.0002490 0.0250865 0.0250858 0.3021480 0.4635760 361141
23 154929412 rs557132 C T -0.0005303 0.0002648 0.0452324 0.0452314 0.2454490 0.3568210 361141
23 154929637 rs35185538 CT C -0.0005284 0.0002764 0.0559165 0.0559155 0.2296910 0.3011920 361141
23 154929952 rs4012982 CAA C -0.0006042 0.0002785 0.0300615 0.0300608 0.2394150 0.3165560 361141
23 154930230 rs781880 A G -0.0005223 0.0002648 0.0485333 0.0485326 0.2458590 0.3618540 361141
23 154930487 rs781879 T A -0.0015671 0.0009084 0.0844948 0.0844939 0.0195590 0.1263580 361141

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.110637 ES:SE:LP:AF:SS:ID   8.05313e-05:0.000483583:0.0616104:0.110637:361141:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.115823 ES:SE:LP:AF:SS:ID   0.000185209:0.000456942:0.164157:0.115823:361141:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0972984    ES:SE:LP:AF:SS:ID   -0.000386479:0.000513693:0.345017:0.0972984:361141:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.0156864    ES:SE:LP:AF:SS:ID   0.00151328:0.0012258:0.663522:0.0156864:361141:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0564574    ES:SE:LP:AF:SS:ID   0.000276545:0.000636585:0.177842:0.0564574:361141:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121731 ES:SE:LP:AF:SS:ID   1.5899e-05:0.000433364:0.0128998:0.121731:361141:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121162 ES:SE:LP:AF:SS:ID   9.74426e-05:0.000462271:0.0793284:0.121162:361141:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.120958 ES:SE:LP:AF:SS:ID   1.37854e-05:0.000433597:0.0111571:0.120958:361141:rs141242758
1   740284  rs61770167  C   T   .   PASS    AF=0.0057837    ES:SE:LP:AF:SS:ID   -0.000578775:0.00198384:0.113238:0.0057837:361141:rs61770167
1   749963  rs529266287 T   TAA .   PASS    AF=0.86975  ES:SE:LP:AF:SS:ID   5.54621e-05:0.000427457:0.0473213:0.86975:361141:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.122909 ES:SE:LP:AF:SS:ID   -3.42627e-05:0.00042334:0.028959:0.122909:361141:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142706 ES:SE:LP:AF:SS:ID   -3.35746e-05:0.000418445:0.0287014:0.142706:361141:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123024 ES:SE:LP:AF:SS:ID   -3.41765e-05:0.000422751:0.0289251:0.123024:361141:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.870898 ES:SE:LP:AF:SS:ID   0.00013681:0.000411956:0.130877:0.870898:361141:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.875479 ES:SE:LP:AF:SS:ID   0.000119655:0.000418783:0.110647:0.875479:361141:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128624 ES:SE:LP:AF:SS:ID   -0.000123852:0.000412989:0.116759:0.128624:361141:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0363584    ES:SE:LP:AF:SS:ID   0.000536997:0.000749264:0.324625:0.0363584:361141:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870491 ES:SE:LP:AF:SS:ID   0.000131488:0.000411528:0.125322:0.870491:361141:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870596 ES:SE:LP:AF:SS:ID   0.000133219:0.000411715:0.127108:0.870596:361141:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870488 ES:SE:LP:AF:SS:ID   0.00013519:0.000411512:0.129292:0.870488:361141:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.00504412   ES:SE:LP:AF:SS:ID   -0.000241277:0.00211243:0.0414051:0.00504412:361141:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.00501109   ES:SE:LP:AF:SS:ID   -0.000211166:0.00211808:0.0359361:0.00501109:361141:rs142682604
1   755435  rs184270342 T   G   .   PASS    AF=0.0055852    ES:SE:LP:AF:SS:ID   0.000201094:0.00204633:0.0354019:0.0055852:361141:rs184270342
1   755890  rs3115858   A   T   .   PASS    AF=0.870571 ES:SE:LP:AF:SS:ID   0.000139614:0.000410788:0.13433:0.870571:361141:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.125978 ES:SE:LP:AF:SS:ID   -9.36198e-05:0.000419154:0.084463:0.125978:361141:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.870134 ES:SE:LP:AF:SS:ID   0.000132677:0.000409799:0.127191:0.870134:361141:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869421 ES:SE:LP:AF:SS:ID   0.00017329:0.000409426:0.172558:0.869421:361141:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870288 ES:SE:LP:AF:SS:ID   0.000144623:0.000410152:0.140032:0.870288:361141:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870294 ES:SE:LP:AF:SS:ID   0.000143664:0.000410181:0.138971:0.870294:361141:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870303 ES:SE:LP:AF:SS:ID   0.000143375:0.000410194:0.13865:0.870303:361141:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870746 ES:SE:LP:AF:SS:ID   0.000143637:0.000411239:0.138538:0.870746:361141:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0988405    ES:SE:LP:AF:SS:ID   -0.000405065:0.000477875:0.401602:0.0988405:361141:rs10157329
1   759600  rs545998451 AGT A   .   PASS    AF=0.00647403   ES:SE:LP:AF:SS:ID   -0.00145204:0.00184874:0.364309:0.00647403:361141:1_759600_AGT_A
1   759837  rs3115851   T   A   .   PASS    AF=0.874642 ES:SE:LP:AF:SS:ID   0.000117473:0.000417368:0.108821:0.874642:361141:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.864035 ES:SE:LP:AF:SS:ID   9.68667e-05:0.00040929:0.0899559:0.864035:361141:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.869432 ES:SE:LP:AF:SS:ID   0.000107255:0.000413264:0.0995094:0.869432:361141:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866377 ES:SE:LP:AF:SS:ID   0.000113573:0.000413348:0.105962:0.866377:361141:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0987764    ES:SE:LP:AF:SS:ID   -6.27868e-05:0.000494861:0.0462224:0.0987764:361141:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.871562 ES:SE:LP:AF:SS:ID   8.03709e-05:0.000418058:0.0718357:0.871562:361141:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.871562 ES:SE:LP:AF:SS:ID   8.03776e-05:0.000418057:0.0718424:0.871562:361141:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.871561 ES:SE:LP:AF:SS:ID   7.98952e-05:0.000418065:0.0713778:0.871561:361141:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871936 ES:SE:LP:AF:SS:ID   5.98712e-05:0.000418304:0.0524736:0.871936:361141:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125555 ES:SE:LP:AF:SS:ID   -9.79496e-05:0.000418634:0.0888397:0.125555:361141:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105422 ES:SE:LP:AF:SS:ID   -0.000197752:0.000455741:0.177602:0.105422:361141:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855383 ES:SE:LP:AF:SS:ID   0.000246866:0.000409135:0.262606:0.855383:361141:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838362 ES:SE:LP:AF:SS:ID   -2.42103e-05:0.000403062:0.0213161:0.838362:361141:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.763359 ES:SE:LP:AF:SS:ID   7.70375e-05:0.000324395:0.0902921:0.763359:361141:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.10592  ES:SE:LP:AF:SS:ID   9.14749e-05:0.000446927:0.0768462:0.10592:361141:rs12562034
1   768819  rs12562811  C   T   .   PASS    AF=0.00768507   ES:SE:LP:AF:SS:ID   0.00221598:0.00166042:0.7399:0.00768507:361141:rs12562811
1   769138  rs59306077  CAT C   .   PASS    AF=0.12893  ES:SE:LP:AF:SS:ID   -8.76256e-05:0.000412778:0.0799357:0.12893:361141:rs762168062