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_24423108/ukb-d-22601_24423108.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_24423108/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 14:59:38 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-22601_24423108/ukb-d-22601_24423108.vcf.gz ...
Read summary statistics for 9954297 SNPs.
Dropped 7676 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, 1277743 SNPs remain.
After merging with regression SNP LD, 1277743 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0045 (0.0049)
Lambda GC: 1.0119
Mean Chi^2: 1.0176
Intercept: 1.0096 (0.0064)
Ratio: 0.5446 (0.3659)
Analysis finished at Mon Nov 25 15:01:18 2019
Total time elapsed: 1.0m:40.9s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9414,
    "inflation_factor": 1.004,
    "mean_EFFECT": -4.6886e-07,
    "n": 89866,
    "n_snps": 9954297,
    "n_clumped_hits": 1,
    "n_p_sig": 26,
    "n_mono": 0,
    "n_ns": 1080008,
    "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": 196971,
    "n_est": 89876.7293,
    "ratio_se_n": 1.0001,
    "mean_diff": -3.9315e-06,
    "ratio_diff": 5.3774,
    "sd_y_est1": 0.1278,
    "sd_y_est2": 0.1278,
    "r2_sum1": 6.8462e-06,
    "r2_sum2": 0.0004,
    "r2_sum3": 0.0004,
    "r2_sum4": 0.0004,
    "ldsc_nsnp_merge_refpanel_ld": 1277743,
    "ldsc_nsnp_merge_regression_ld": 1277743,
    "ldsc_observed_scale_h2_beta": 0.0045,
    "ldsc_observed_scale_h2_se": 0.0049,
    "ldsc_intercept_beta": 1.0096,
    "ldsc_intercept_se": 0.0064,
    "ldsc_lambda_gc": 1.0119,
    "ldsc_mean_chisq": 1.0176,
    "ldsc_ratio": 0.5455
}

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 TRUE
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 9947171 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 98 0 48025 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 30064 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.080813e+00 6.177043e+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 7.875659e+07 5.609364e+07 3.02000e+02 3.246955e+07 6.965739e+07 1.147371e+08 2.492297e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -5.000000e-07 1.441300e-03 -1.30348e-02 -6.750000e-04 -8.400000e-06 6.559000e-04 1.544640e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 1.233400e-03 7.425000e-04 4.98400e-04 6.632000e-04 9.002000e-04 1.593600e-03 4.273600e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.989533e-01 2.885973e-01 0.00000e+00 2.490090e-01 4.991511e-01 7.483418e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.989515e-01 2.885984e-01 0.00000e+00 2.490064e-01 4.991486e-01 7.483412e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.556051e-01 2.629493e-01 8.40060e-03 3.926980e-02 1.508390e-01 4.076420e-01 9.915990e-01 ▇▂▂▁▁
numeric AF_reference 196971 0.9802125 NA NA NA NA NA 2.552368e-01 2.537796e-01 0.00000e+00 4.392970e-02 1.667330e-01 4.037540e-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 0.0004888 0.0010529 0.6424894 0.6424871 0.1114870 0.1894970 89866
1 693731 rs12238997 A G 0.0004662 0.0009974 0.6401636 0.6401628 0.1164980 0.1417730 89866
1 707522 rs371890604 G C 0.0017905 0.0011212 0.1102900 0.1102848 0.0978285 0.1293930 89866
1 717587 rs144155419 G A -0.0003387 0.0026708 0.8990760 0.8990760 0.0158090 0.0045926 89866
1 730087 rs148120343 T C -0.0006562 0.0013830 0.6351715 0.6351714 0.0569661 0.0127796 89866
1 731718 rs142557973 T C 0.0006069 0.0009465 0.5213531 0.5213510 0.1223780 0.1543530 89866
1 732032 rs61770163 A C 0.0013242 0.0010090 0.1893948 0.1893922 0.1220530 0.1555510 89866
1 734349 rs141242758 T C 0.0005715 0.0009472 0.5462482 0.5462467 0.1216170 0.1525560 89866
1 749963 rs529266287 T TAA -0.0003673 0.0009319 0.6934721 0.6934712 0.8689590 0.7641770 89866
1 751343 rs28544273 T A 0.0008424 0.0009238 0.3618462 0.3618437 0.1236780 0.2426120 89866
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T 0.0003933 0.0011078 0.7225868 0.7225849 0.0553576 0.0309934 89866
23 154923374 rs111332691 T A -0.0008159 0.0012060 0.4987065 0.4987029 0.0449113 0.0116556 89866
23 154925045 rs509981 C T 0.0004095 0.0005816 0.4814199 0.4814189 0.2444280 0.3634440 89866
23 154925895 rs538470 C T 0.0003803 0.0005947 0.5225634 0.5225611 0.2408760 0.3634440 89866
23 154927581 rs644138 G A 0.0004719 0.0005471 0.3884320 0.3884295 0.3001910 0.4635760 89866
23 154929412 rs557132 C T 0.0003769 0.0005818 0.5170934 0.5170908 0.2442750 0.3568210 89866
23 154929637 rs35185538 CT C 0.0003583 0.0006075 0.5553055 0.5553039 0.2286930 0.3011920 89866
23 154929952 rs4012982 CAA C 0.0004331 0.0006123 0.4793525 0.4793508 0.2381980 0.3165560 89866
23 154930230 rs781880 A G 0.0003396 0.0005816 0.5593324 0.5593325 0.2447120 0.3618540 89866
23 154930487 rs781879 T A -0.0021649 0.0019730 0.2725323 0.2725288 0.0196791 0.1263580 89866

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.111487 ES:SE:LP:AF:SS:ID   0.000488777:0.00105289:0.192134:0.111487:89866:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.116498 ES:SE:LP:AF:SS:ID   0.000466245:0.000997377:0.193709:0.116498:89866:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0978285    ES:SE:LP:AF:SS:ID   0.00179054:0.00112125:0.957464:0.0978285:89866:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.015809 ES:SE:LP:AF:SS:ID   -0.000338734:0.0026708:0.0462036:0.015809:89866:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0569661    ES:SE:LP:AF:SS:ID   -0.000656184:0.00138301:0.197109:0.0569661:89866:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122378 ES:SE:LP:AF:SS:ID   0.000606931:0.00094646:0.282868:0.122378:89866:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.122053 ES:SE:LP:AF:SS:ID   0.00132423:0.00100903:0.722632:0.122053:89866:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.121617 ES:SE:LP:AF:SS:ID   0.000571513:0.000947164:0.26261:0.121617:89866:rs141242758
1   749963  rs529266287 T   TAA .   PASS    AF=0.868959 ES:SE:LP:AF:SS:ID   -0.000367295:0.000931869:0.158971:0.868959:89866:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.123678 ES:SE:LP:AF:SS:ID   0.000842384:0.000923811:0.441476:0.123678:89866:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.143191 ES:SE:LP:AF:SS:ID   0.000319069:0.000914628:0.138345:0.143191:89866:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123813 ES:SE:LP:AF:SS:ID   0.000853203:0.000922478:0.449748:0.123813:89866:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.869927 ES:SE:LP:AF:SS:ID   -0.000699147:0.000898824:0.359855:0.869927:89866:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.87461  ES:SE:LP:AF:SS:ID   -0.000988021:0.000913475:0.553725:0.87461:89866:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.12951  ES:SE:LP:AF:SS:ID   0.000631791:0.000901572:0.315648:0.12951:89866:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0357365    ES:SE:LP:AF:SS:ID   0.000385105:0.00164944:0.0886336:0.0357365:89866:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869574 ES:SE:LP:AF:SS:ID   -0.000738031:0.000898032:0.385973:0.869574:89866:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869652 ES:SE:LP:AF:SS:ID   -0.000749439:0.000898431:0.393413:0.869652:89866:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.86957  ES:SE:LP:AF:SS:ID   -0.000737164:0.000897996:0.385413:0.86957:89866:rs3131967
1   755890  rs3115858   A   T   .   PASS    AF=0.869566 ES:SE:LP:AF:SS:ID   -0.000695068:0.000896216:0.358514:0.869566:89866:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.126879 ES:SE:LP:AF:SS:ID   0.000628852:0.000914897:0.308153:0.126879:89866:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.869094 ES:SE:LP:AF:SS:ID   -0.000690958:0.000893892:0.357004:0.869094:89866:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.868498 ES:SE:LP:AF:SS:ID   -0.000794505:0.000893345:0.427347:0.868498:89866:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869245 ES:SE:LP:AF:SS:ID   -0.00071117:0.000894709:0.369881:0.869245:89866:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869251 ES:SE:LP:AF:SS:ID   -0.000710807:0.00089477:0.369609:0.869251:89866:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869252 ES:SE:LP:AF:SS:ID   -0.000710663:0.000894776:0.369511:0.869252:89866:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869724 ES:SE:LP:AF:SS:ID   -0.000718539:0.000897156:0.373468:0.869724:89866:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0991203    ES:SE:LP:AF:SS:ID   0.000907188:0.00104444:0.414456:0.0991203:89866:rs10157329
1   759837  rs3115851   T   A   .   PASS    AF=0.873732 ES:SE:LP:AF:SS:ID   -0.000915352:0.000910481:0.50206:0.873732:89866:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.863116 ES:SE:LP:AF:SS:ID   -0.000813587:0.000893043:0.440952:0.863116:89866:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.868511 ES:SE:LP:AF:SS:ID   -0.000762754:0.000901965:0.400394:0.868511:89866:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.86539  ES:SE:LP:AF:SS:ID   -0.000682434:0.000902098:0.347411:0.86539:89866:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0994301    ES:SE:LP:AF:SS:ID   0.00152532:0.00108026:0.801472:0.0994301:89866:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.870696 ES:SE:LP:AF:SS:ID   -0.000819353:0.000912474:0.432718:0.870696:89866:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.870695 ES:SE:LP:AF:SS:ID   -0.000819336:0.000912474:0.432708:0.870695:89866:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.870693 ES:SE:LP:AF:SS:ID   -0.000818916:0.000912477:0.432417:0.870693:89866:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871061 ES:SE:LP:AF:SS:ID   -0.000820554:0.000912908:0.433276:0.871061:89866:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.126484 ES:SE:LP:AF:SS:ID   0.000776317:0.000913831:0.40275:0.126484:89866:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.106022 ES:SE:LP:AF:SS:ID   0.00090936:0.000993408:0.443712:0.106022:89866:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.854451 ES:SE:LP:AF:SS:ID   -0.000838932:0.00089283:0.459159:0.854451:89866:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.839008 ES:SE:LP:AF:SS:ID   -0.000572343:0.000882025:0.287008:0.839008:89866:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.76317  ES:SE:LP:AF:SS:ID   -0.000851378:0.000709139:0.638429:0.76317:89866:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.10519  ES:SE:LP:AF:SS:ID   0.000670962:0.000977307:0.307706:0.10519:89866:rs12562034
1   769138  rs59306077  CAT C   .   PASS    AF=0.129876 ES:SE:LP:AF:SS:ID   0.000646332:0.000901031:0.324977:0.129876:89866:rs762168062
1   769223  rs60320384  C   G   .   PASS    AF=0.129602 ES:SE:LP:AF:SS:ID   0.000537923:0.000899853:0.259652:0.129602:89866:rs60320384
1   769963  rs7518545   G   A   .   PASS    AF=0.104469 ES:SE:LP:AF:SS:ID   0.000764149:0.000984843:0.358719:0.104469:89866:rs7518545
1   770181  rs146076599 A   G   .   PASS    AF=0.00900456   ES:SE:LP:AF:SS:ID   0.00188937:0.00347535:0.231595:0.00900456:89866:rs146076599
1   770886  rs371458725 G   A   .   PASS    AF=0.103468 ES:SE:LP:AF:SS:ID   0.000674397:0.000993389:0.303458:0.103468:89866:rs371458725
1   771410  rs2519006   C   T   .   PASS    AF=0.829371 ES:SE:LP:AF:SS:ID   -0.000675479:0.000878324:0.354712:0.829371:89866:rs2519006
1   771823  rs2977605   T   C   .   PASS    AF=0.869112 ES:SE:LP:AF:SS:ID   -0.000726002:0.00089551:0.37931:0.869112:89866:rs2977605