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-K11_LIVER/ukb-d-K11_LIVER.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-K11_LIVER/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 14:59:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/dev/ukb-d-import/processed/ukb-d-K11_LIVER/ukb-d-K11_LIVER.vcf.gz ...
Read summary statistics for 10138607 SNPs.
Dropped 7900 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, 1279028 SNPs remain.
After merging with regression SNP LD, 1279028 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0018 (0.0012)
Lambda GC: 1.0135
Mean Chi^2: 1.0132
Intercept: 1.0008 (0.0061)
Ratio: 0.0593 (0.4605)
Analysis finished at Mon Nov 25 15:01:36 2019
Total time elapsed: 2.0m:26.22s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9423,
    "inflation_factor": 1.014,
    "mean_EFFECT": -2.1811e-07,
    "n": 361194,
    "n_snps": 10138607,
    "n_clumped_hits": 2,
    "n_p_sig": 69,
    "n_mono": 0,
    "n_ns": 1091303,
    "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": 199918,
    "n_est": 361186.8076,
    "ratio_se_n": 1,
    "mean_diff": -3.1099e-06,
    "ratio_diff": 26.7953,
    "sd_y_est1": 0.0679,
    "sd_y_est2": 0.0679,
    "r2_sum1": 1.4109e-06,
    "r2_sum2": 0.0003,
    "r2_sum3": 0.0003,
    "r2_sum4": 0.0003,
    "ldsc_nsnp_merge_refpanel_ld": 1279028,
    "ldsc_nsnp_merge_regression_ld": 1279028,
    "ldsc_observed_scale_h2_beta": 0.0018,
    "ldsc_observed_scale_h2_se": 0.0012,
    "ldsc_intercept_beta": 1.0008,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.0135,
    "ldsc_mean_chisq": 1.0132,
    "ldsc_ratio": 0.0606
}

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 10131263 0 NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 100 0 48611 0 NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 342 0 30325 0 NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA 9.080238e+00 6.178080e+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 7.876344e+07 5.607160e+07 3.02000e+02 3.250467e+07 6.967819e+07 1.147263e+08 2.492297e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA -2.000000e-07 3.995000e-04 -3.80220e-03 -1.821000e-04 -1.700000e-06 1.791000e-04 4.970600e-03 ▁▂▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA 3.378000e-04 2.109000e-04 1.31500e-04 1.765000e-04 2.424000e-04 4.389000e-04 1.206900e-03 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA 4.979647e-01 2.888905e-01 0.00000e+00 2.475435e-01 4.970053e-01 7.479111e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA 4.979643e-01 2.888908e-01 0.00000e+00 2.475427e-01 4.970042e-01 7.479103e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA 2.514244e-01 2.629400e-01 7.51660e-03 3.615680e-02 1.447640e-01 4.014980e-01 9.924830e-01 ▇▂▂▁▁
numeric AF_reference 199918 0.9802815 NA NA NA NA NA 2.511329e-01 2.537967e-01 0.00000e+00 3.973640e-02 1.609420e-01 3.979630e-01 1.000000e+00 ▇▃▂▁▁
numeric N 0 1.0000000 NA NA NA NA NA 3.611940e+05 0.000000e+00 3.61194e+05 3.611940e+05 3.611940e+05 3.611940e+05 3.611940e+05 ▁▁▇▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 692794 rs530212009 CA C 0.0009424 0.0002800 0.0007633 0.0007633 0.1106400 0.1894970 361194
1 693731 rs12238997 A G 0.0010537 0.0002646 0.0000682 0.0000681 0.1158300 0.1417730 361194
1 707522 rs371890604 G C 0.0008536 0.0002974 0.0041047 0.0041045 0.0973034 0.1293930 361194
1 717587 rs144155419 G A 0.0021113 0.0007098 0.0029333 0.0029331 0.0156880 0.0045926 361194
1 730087 rs148120343 T C 0.0007709 0.0003686 0.0364914 0.0364905 0.0564602 0.0127796 361194
1 731718 rs142557973 T C 0.0009841 0.0002509 0.0000878 0.0000878 0.1217380 0.1543530 361194
1 732032 rs61770163 A C 0.0009230 0.0002677 0.0005637 0.0005637 0.1211710 0.1555510 361194
1 734349 rs141242758 T C 0.0009751 0.0002511 0.0001028 0.0001028 0.1209650 0.1525560 361194
1 749963 rs529266287 T TAA -0.0009405 0.0002475 0.0001447 0.0001447 0.8697420 0.7641770 361194
1 751343 rs28544273 T A 0.0008283 0.0002451 0.0007268 0.0007267 0.1229160 0.2426120 361194
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
23 154923311 rs141127553 C T -0.0001843 0.0002899 0.5250069 0.5250067 0.0561667 0.0309934 361194
23 154923374 rs111332691 T A 0.0002626 0.0003186 0.4098604 0.4098593 0.0447862 0.0116556 361194
23 154925045 rs509981 C T -0.0000598 0.0001533 0.6963138 0.6963127 0.2456060 0.3634440 361194
23 154925895 rs538470 C T -0.0000503 0.0001568 0.7485296 0.7485308 0.2419150 0.3634440 361194
23 154927581 rs644138 G A -0.0001036 0.0001442 0.4725035 0.4725014 0.3021620 0.4635760 361194
23 154929412 rs557132 C T -0.0000573 0.0001533 0.7088478 0.7088467 0.2454590 0.3568210 361194
23 154929637 rs35185538 CT C -0.0000723 0.0001600 0.6514799 0.6514802 0.2296970 0.3011920 361194
23 154929952 rs4012982 CAA C -0.0000381 0.0001613 0.8131019 0.8131013 0.2394250 0.3165560 361194
23 154930230 rs781880 A G -0.0000590 0.0001533 0.7004353 0.7004357 0.2458690 0.3618540 361194
23 154930487 rs781879 T A 0.0006445 0.0005259 0.2204190 0.2204179 0.0195623 0.1263580 361194

bcf preview

1   692794  rs530212009 CA  C   .   PASS    AF=0.11064  ES:SE:LP:AF:SS:ID   0.000942447:0.000280008:3.11729:0.11064:361194:1_692794_CA_C
1   693731  rs12238997  A   G   .   PASS    AF=0.11583  ES:SE:LP:AF:SS:ID   0.0010537:0.000264571:4.16651:0.11583:361194:rs12238997
1   707522  rs371890604 G   C   .   PASS    AF=0.0973034    ES:SE:LP:AF:SS:ID   0.000853646:0.000297436:2.38672:0.0973034:361194:rs371890604
1   717587  rs144155419 G   A   .   PASS    AF=0.015688 ES:SE:LP:AF:SS:ID   0.00211129:0.000709757:2.53265:0.015688:361194:rs144155419
1   730087  rs148120343 T   C   .   PASS    AF=0.0564602    ES:SE:LP:AF:SS:ID   0.000770874:0.000368589:1.43781:0.0564602:361194:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121738 ES:SE:LP:AF:SS:ID   0.000984131:0.00025092:4.05651:0.121738:361194:rs58276399
1   732032  rs61770163  A   C   .   PASS    AF=0.121171 ES:SE:LP:AF:SS:ID   0.000923018:0.000267656:3.24893:0.121171:361194:rs61770163
1   734349  rs141242758 T   C   .   PASS    AF=0.120965 ES:SE:LP:AF:SS:ID   0.000975076:0.000251055:3.98797:0.120965:361194:rs141242758
1   749963  rs529266287 T   TAA .   PASS    AF=0.869742 ES:SE:LP:AF:SS:ID   -0.000940492:0.000247501:3.83939:0.869742:361194:rs529266287
1   751343  rs28544273  T   A   .   PASS    AF=0.122916 ES:SE:LP:AF:SS:ID   0.000828329:0.000245118:3.1386:0.122916:361194:rs28544273
1   751488  rs200141114 G   GA  .   PASS    AF=0.142712 ES:SE:LP:AF:SS:ID   0.000743498:0.000242287:2.6675:0.142712:361194:rs200141114
1   751756  rs28527770  T   C   .   PASS    AF=0.123031 ES:SE:LP:AF:SS:ID   0.000819397:0.000244777:3.0886:0.123031:361194:rs28527770
1   753405  rs3115860   C   A   .   PASS    AF=0.87089  ES:SE:LP:AF:SS:ID   -0.000858723:0.000238527:3.49741:0.87089:361194:rs3115860
1   753425  rs3131970   T   C   .   PASS    AF=0.875472 ES:SE:LP:AF:SS:ID   -0.000855451:0.000242479:3.37792:0.875472:361194:rs3131970
1   753541  rs2073813   G   A   .   PASS    AF=0.128631 ES:SE:LP:AF:SS:ID   0.000808243:0.000239125:3.1397:0.128631:361194:rs2073813
1   754105  rs12184325  C   T   .   PASS    AF=0.0363613    ES:SE:LP:AF:SS:ID   -0.000343614:0.000433835:0.368213:0.0363613:361194:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870484 ES:SE:LP:AF:SS:ID   -0.00083513:0.000238279:3.34015:0.870484:361194:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870589 ES:SE:LP:AF:SS:ID   -0.000839534:0.000238388:3.36773:0.870589:361194:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.870481 ES:SE:LP:AF:SS:ID   -0.000835439:0.00023827:3.34248:0.870481:361194:rs3131967
1   755890  rs3115858   A   T   .   PASS    AF=0.870563 ES:SE:LP:AF:SS:ID   -0.000849533:0.00023785:3.45013:0.870563:361194:rs3115858
1   756434  rs61768170  G   C   .   PASS    AF=0.125985 ES:SE:LP:AF:SS:ID   0.000875867:0.000242695:3.51214:0.125985:361194:rs61768170
1   756604  rs3131962   A   G   .   PASS    AF=0.870127 ES:SE:LP:AF:SS:ID   -0.000875252:0.000237278:3.64702:0.870127:361194:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869414 ES:SE:LP:AF:SS:ID   -0.000852229:0.000237062:3.48882:0.869414:361194:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.870281 ES:SE:LP:AF:SS:ID   -0.000861792:0.000237482:3.54561:0.870281:361194:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870287 ES:SE:LP:AF:SS:ID   -0.000861631:0.000237499:3.54403:0.870287:361194:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870295 ES:SE:LP:AF:SS:ID   -0.000861776:0.000237506:3.54487:0.870295:361194:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.870739 ES:SE:LP:AF:SS:ID   -0.000855401:0.000238111:3.48461:0.870739:361194:rs3131954
1   759293  rs10157329  T   A   .   PASS    AF=0.0988442    ES:SE:LP:AF:SS:ID   0.000867554:0.000276701:2.76532:0.0988442:361194:rs10157329
1   759837  rs3115851   T   A   .   PASS    AF=0.874635 ES:SE:LP:AF:SS:ID   -0.000884981:0.00024166:3.60171:0.874635:361194:rs3115851
1   761732  rs2286139   C   T   .   PASS    AF=0.864027 ES:SE:LP:AF:SS:ID   -0.000823444:0.000236984:3.29115:0.864027:361194:rs2286139
1   761752  rs1057213   C   T   .   PASS    AF=0.869425 ES:SE:LP:AF:SS:ID   -0.000838367:0.000239284:3.33819:0.869425:361194:rs1057213
1   762273  rs3115849   G   A   .   PASS    AF=0.866371 ES:SE:LP:AF:SS:ID   -0.000844966:0.000239333:3.38214:0.866371:361194:rs3115849
1   762485  rs12095200  C   A   .   PASS    AF=0.0987831    ES:SE:LP:AF:SS:ID   0.00109856:0.000286525:3.89944:0.0987831:361194:rs12095200
1   762589  rs3115848   G   C   .   PASS    AF=0.871556 ES:SE:LP:AF:SS:ID   -0.000889863:0.000242059:3.62571:0.871556:361194:rs3115848
1   762592  rs3131950   C   G   .   PASS    AF=0.871556 ES:SE:LP:AF:SS:ID   -0.00088981:0.000242059:3.62534:0.871556:361194:rs3131950
1   762601  rs3131949   T   C   .   PASS    AF=0.871555 ES:SE:LP:AF:SS:ID   -0.000889866:0.000242064:3.62561:0.871555:361194:rs3131949
1   762632  rs3131948   T   A   .   PASS    AF=0.871929 ES:SE:LP:AF:SS:ID   -0.000868253:0.000242202:3.47193:0.871929:361194:rs3131948
1   764191  rs7515915   T   G   .   PASS    AF=0.125562 ES:SE:LP:AF:SS:ID   0.000847466:0.000242393:3.32613:0.125562:361194:rs7515915
1   766007  rs61768174  A   C   .   PASS    AF=0.105428 ES:SE:LP:AF:SS:ID   0.000773637:0.00026388:2.47231:0.105428:361194:rs61768174
1   766105  rs2519015   T   A   .   PASS    AF=0.855376 ES:SE:LP:AF:SS:ID   -0.000829521:0.000236895:3.33493:0.855376:361194:rs2519015
1   768116  rs376645387 A   AGTTTT  .   PASS    AF=0.838358 ES:SE:LP:AF:SS:ID   5.62636e-05:0.000233388:0.0917842:0.838358:361194:rs376645387
1   768253  rs2977608   A   C   .   PASS    AF=0.763351 ES:SE:LP:AF:SS:ID   -0.000347077:0.000187833:1.18955:0.763351:361194:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.105921 ES:SE:LP:AF:SS:ID   -0.000365596:0.000258788:0.802064:0.105921:361194:rs12562034
1   768819  rs12562811  C   T   .   PASS    AF=0.00768395   ES:SE:LP:AF:SS:ID   -0.000924912:0.000961516:0.473551:0.00768395:361194:rs12562811
1   769138  rs59306077  CAT C   .   PASS    AF=0.128938 ES:SE:LP:AF:SS:ID   0.000809142:0.000239003:3.14839:0.128938:361194:rs762168062
1   769223  rs60320384  C   G   .   PASS    AF=0.128608 ES:SE:LP:AF:SS:ID   0.000824522:0.000238782:3.25619:0.128608:361194:rs60320384
1   769963  rs7518545   G   A   .   PASS    AF=0.105213 ES:SE:LP:AF:SS:ID   -0.000371483:0.000260743:0.8118:0.105213:361194:rs7518545
1   770181  rs146076599 A   G   .   PASS    AF=0.009077 ES:SE:LP:AF:SS:ID   0.00186055:0.000919232:1.36685:0.009077:361194:rs146076599
1   770377  rs112563271 A   T   .   PASS    AF=0.00769858   ES:SE:LP:AF:SS:ID   -0.000926817:0.000965783:0.472074:0.00769858:361194:rs112563271
1   770886  rs371458725 G   A   .   PASS    AF=0.104268 ES:SE:LP:AF:SS:ID   -0.000341386:0.000262827:0.712245:0.104268:361194:rs371458725