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-22601_23213026/ukb-d-22601_23213026.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-22601_23213026/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 15:00:54 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22601_23213026/ukb-d-22601_23213026.vcf.gz ...
Read summary statistics for 9547567 SNPs.
Dropped 7118 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, 1273790 SNPs remain.
After merging with regression SNP LD, 1273790 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0142 (0.0051)
Lambda GC: 1.0179
Mean Chi^2: 1.0213
Intercept: 0.9963 (0.0061)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Mon Nov 25 15:02:29 2019
Total time elapsed: 1.0m:35.27s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9392,
    "inflation_factor": 1.0192,
    "mean_EFFECT": -2.8142e-06,
    "n": 89866,
    "n_snps": 9547567,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "n_mono": 0,
    "n_ns": 1053915,
    "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": 190842,
    "n_est": 89853.0991,
    "ratio_se_n": 0.9999,
    "mean_diff": -0,
    "ratio_diff": 23.0171,
    "sd_y_est1": 0.1134,
    "sd_y_est2": 0.1134,
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1273790,
    "ldsc_nsnp_merge_regression_ld": 1273790,
    "ldsc_observed_scale_h2_beta": 0.0142,
    "ldsc_observed_scale_h2_se": 0.0051,
    "ldsc_intercept_beta": 0.9963,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.0179,
    "ldsc_mean_chisq": 1.0213,
    "ldsc_ratio": -0.1737
}

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 9540988 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 98 0 46569 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 29488 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.080383e+00 6.174398e+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 7.871473e+07 5.611223e+07 3.02000e+02 3.241943e+07 6.958996e+07 1.147308e+08 2.492297e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -2.800000e-06 1.170700e-03 -1.13611e-02 -5.835000e-04 -8.100000e-06 5.669000e-04 1.157160e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.020800e-03 5.645000e-04 4.42200e-04 5.841000e-04 7.721000e-04 1.299600e-03 3.160900e-03 ▇▂▂▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.969404e-01 2.891226e-01 1.00000e-07 2.462448e-01 4.959113e-01 7.471005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.969386e-01 2.891237e-01 1.00000e-07 2.462415e-01 4.959091e-01 7.470989e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.653647e-01 2.627906e-01 1.07296e-02 4.711800e-02 1.647970e-01 4.218180e-01 9.892700e-01 ▇▂▂▁▁
numeric AF_reference 190842 0.9800115 NA NA NA NA NA 2.647644e-01 2.535867e-01 0.00000e+00 5.391370e-02 1.795130e-01 4.167330e-01 1.000000e+00 ▇▃▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA 8.986600e+04 0.000000e+00 8.98660e+04 8.986600e+04 8.986600e+04 8.986600e+04 8.986600e+04 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0009614 0.0009343 0.3034918 0.3034888 0.1114870 0.1894970 89866
1 693731 rs12238997 A G 0.0007256 0.0008850 0.4123006 0.4122992 0.1164980 0.1417730 89866
1 707522 rs371890604 G C 0.0013655 0.0009950 0.1699292 0.1699262 0.0978285 0.1293930 89866
1 717587 rs144155419 G A 0.0008464 0.0023699 0.7209747 0.7209745 0.0158090 0.0045926 89866
1 730087 rs148120343 T C -0.0000381 0.0012272 0.9752240 0.9752241 0.0569661 0.0127796 89866
1 731718 rs142557973 T C 0.0005583 0.0008398 0.5062057 0.5062043 0.1223780 0.1543530 89866
1 732032 rs61770163 A C 0.0011416 0.0008954 0.2023010 0.2022990 0.1220530 0.1555510 89866
1 734349 rs141242758 T C 0.0005566 0.0008405 0.5077770 0.5077754 0.1216170 0.1525560 89866
1 749963 rs529266287 T TAA -0.0013500 0.0008269 0.1025390 0.1025351 0.8689590 0.7641770 89866
1 751343 rs28544273 T A 0.0013315 0.0008197 0.1043179 0.1043137 0.1236780 0.2426120 89866
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0002013 0.0009830 0.8377200 0.8377201 0.0553576 0.0309934 89866
23 154923374 rs111332691 T A -0.0010679 0.0010702 0.3183237 0.3183200 0.0449113 0.0116556 89866
23 154925045 rs509981 C T -0.0000145 0.0005161 0.9776460 0.9776457 0.2444280 0.3634440 89866
23 154925895 rs538470 C T -0.0000656 0.0005277 0.9010911 0.9010902 0.2408760 0.3634440 89866
23 154927581 rs644138 G A 0.0000187 0.0004855 0.9692149 0.9692147 0.3001910 0.4635760 89866
23 154929412 rs557132 C T -0.0000442 0.0005163 0.9317249 0.9317244 0.2442750 0.3568210 89866
23 154929637 rs35185538 CT C -0.0002507 0.0005391 0.6419423 0.6419413 0.2286930 0.3011920 89866
23 154929952 rs4012982 CAA C -0.0000372 0.0005433 0.9454701 0.9454702 0.2381980 0.3165560 89866
23 154930230 rs781880 A G -0.0000692 0.0005161 0.8933481 0.8933478 0.2447120 0.3618540 89866
23 154930487 rs781879 T A 0.0024485 0.0017507 0.1619571 0.1619527 0.0196791 0.1263580 89866

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111487 ES:SE:LP:AF:SS:ID   0.000961359:0.000934283:0.517853:0.111487:89866:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116498 ES:SE:LP:AF:SS:ID   0.000725589:0.000885022:0.384786:0.116498:89866:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0978285    ES:SE:LP:AF:SS:ID   0.00136551:0.00099495:0.769732:0.0978285:89866:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.015809 ES:SE:LP:AF:SS:ID   0.00084644:0.00236994:0.14208:0.015809:89866:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0569661    ES:SE:LP:AF:SS:ID   -3.81137e-05:0.00122722:0.0108956:0.0569661:89866:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122378 ES:SE:LP:AF:SS:ID   0.000558293:0.000839842:0.295673:0.122378:89866:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122053 ES:SE:LP:AF:SS:ID   0.00114161:0.00089536:0.694002:0.122053:89866:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121617 ES:SE:LP:AF:SS:ID   0.000556646:0.000840467:0.294327:0.121617:89866:rs141242758
1   749963  rs529266287 T   TAA .   PASS    AF=0.868959 ES:SE:LP:AF:SS:ID   -0.00135004:0.000826883:0.989111:0.868959:89866:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.123678 ES:SE:LP:AF:SS:ID   0.00133149:0.000819736:0.981641:0.123678:89866:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143191 ES:SE:LP:AF:SS:ID   0.00137388:0.000811584:1.0434:0.143191:89866:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123813 ES:SE:LP:AF:SS:ID   0.00134062:0.000818553:0.993671:0.123813:89866:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869927 ES:SE:LP:AF:SS:ID   -0.000791458:0.000797571:0.493445:0.869927:89866:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.87461  ES:SE:LP:AF:SS:ID   -0.00124659:0.000810568:0.906326:0.87461:89866:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.12951  ES:SE:LP:AF:SS:ID   0.000890377:0.000800008:0.575563:0.12951:89866:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0357365    ES:SE:LP:AF:SS:ID   -0.00188622:0.00146362:0.70445:0.0357365:89866:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869574 ES:SE:LP:AF:SS:ID   -0.000795254:0.000796868:0.497172:0.869574:89866:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869652 ES:SE:LP:AF:SS:ID   -0.000806147:0.000797223:0.505948:0.869652:89866:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.86957  ES:SE:LP:AF:SS:ID   -0.000794186:0.000796836:0.496311:0.86957:89866:rs3131967
1   755890  rs3115858   A   T   .   PASS    AF=0.869566 ES:SE:LP:AF:SS:ID   -0.000750637:0.000795257:0.461894:0.869566:89866:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126879 ES:SE:LP:AF:SS:ID   0.000953934:0.000811831:0.619821:0.126879:89866:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.869094 ES:SE:LP:AF:SS:ID   -0.000751961:0.000793195:0.464549:0.869094:89866:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868498 ES:SE:LP:AF:SS:ID   -0.000796029:0.00079271:0.501288:0.868498:89866:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869245 ES:SE:LP:AF:SS:ID   -0.000769156:0.000793919:0.478022:0.869245:89866:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869251 ES:SE:LP:AF:SS:ID   -0.000768987:0.000793973:0.47784:0.869251:89866:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869252 ES:SE:LP:AF:SS:ID   -0.000769:0.000793979:0.477847:0.869252:89866:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869724 ES:SE:LP:AF:SS:ID   -0.000769445:0.000796091:0.476537:0.869724:89866:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0991203    ES:SE:LP:AF:SS:ID   0.00109573:0.000926779:0.625085:0.0991203:89866:rs10157329
1   759837  rs3115851   T   A   .   PASS    AF=0.873732 ES:SE:LP:AF:SS:ID   -0.00114775:0.000807912:0.808479:0.873732:89866:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863116 ES:SE:LP:AF:SS:ID   -0.000930186:0.00079244:0.618943:0.863116:89866:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868511 ES:SE:LP:AF:SS:ID   -0.000780781:0.000800359:0.482415:0.868511:89866:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.86539  ES:SE:LP:AF:SS:ID   -0.000765436:0.000800476:0.46985:0.86539:89866:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0994301    ES:SE:LP:AF:SS:ID   0.00109669:0.00095857:0.597586:0.0994301:89866:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870696 ES:SE:LP:AF:SS:ID   -0.00102837:0.000809681:0.690248:0.870696:89866:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870695 ES:SE:LP:AF:SS:ID   -0.00102836:0.000809681:0.690246:0.870695:89866:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870693 ES:SE:LP:AF:SS:ID   -0.00102803:0.000809684:0.689929:0.870693:89866:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871061 ES:SE:LP:AF:SS:ID   -0.0010123:0.000810066:0.674833:0.871061:89866:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126484 ES:SE:LP:AF:SS:ID   0.00110984:0.000810883:0.76674:0.126484:89866:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106022 ES:SE:LP:AF:SS:ID   0.000594475:0.000881504:0.300972:0.106022:89866:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854451 ES:SE:LP:AF:SS:ID   -0.00056007:0.000792256:0.319111:0.854451:89866:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839008 ES:SE:LP:AF:SS:ID   0.00086416:0.000782662:0.569377:0.839008:89866:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.76317  ES:SE:LP:AF:SS:ID   -0.000156058:0.00062926:0.0946716:0.76317:89866:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.10519  ES:SE:LP:AF:SS:ID   -0.000552017:0.000867215:0.280316:0.10519:89866:rs12562034
1   769138  rs59306077  CAT C   .   PASS    AF=0.129876 ES:SE:LP:AF:SS:ID   0.000872924:0.000799528:0.560787:0.129876:89866:rs762168062
1   769223  rs60320384  C   G   .   PASS    AF=0.129602 ES:SE:LP:AF:SS:ID   0.00085929:0.000798482:0.549965:0.129602:89866:rs60320384
1   769963  rs7518545   G   A   .   PASS    AF=0.104469 ES:SE:LP:AF:SS:ID   -0.000490246:0.000873903:0.240476:0.104469:89866:rs7518545
1   770886  rs371458725 G   A   .   PASS    AF=0.103468 ES:SE:LP:AF:SS:ID   -0.000453573:0.000881486:0.216908:0.103468:89866:rs371458725
1   771410  rs2519006   C   T   .   PASS    AF=0.829371 ES:SE:LP:AF:SS:ID   -0.0001656:0.000779384:0.080014:0.829371:89866:rs2519006
1   771823  rs2977605   T   C   .   PASS    AF=0.869112 ES:SE:LP:AF:SS:ID   -0.000727275:0.000794631:0.443613:0.869112:89866:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.129712 ES:SE:LP:AF:SS:ID   0.000824575:0.000798025:0.52074:0.129712:89866:rs59066358