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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_1090.vcf.gz --id UKB-b:3793 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_1090.txt.gz --cohort_controls 310555 --ref /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/human_g1k_v37.fasta --json /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/ukb_gwas.json; 1.1.1",
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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/public/UKB-b-3793/UKB-b-3793_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3793/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:47 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3793/UKB-b-3793_data.vcf.gz ...
Read summary statistics for 9783807 SNPs.
Dropped 14025 SNPs with duplicated rs numbers.
Reading reference panel LD Score from ../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 ../reference/eur_w_ld_chr/[1-22] ...
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1288991 SNPs remain.
After merging with regression SNP LD, 1288991 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0422 (0.0024)
Lambda GC: 1.2493
Mean Chi^2: 1.29
Intercept: 1.0326 (0.0075)
Ratio: 0.1126 (0.0259)
Analysis finished at Thu Oct 17 14:45:35 2019
Total time elapsed: 1.0m:47.62s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9498,
    "inflation_factor": 1.1474,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 7,
    "n_p_sig": 318,
    "n_mono": 0,
    "n_ns": 0,
    "n_mac": 0,
    "is_snpid_unique": true,
    "n_miss_EFFECT": 0,
    "n_miss_SE": 0,
    "n_miss_PVAL": 0,
    "n_miss_AF": 0,
    "n_miss_AF_reference": 175266,
    "n_est": "NA",
    "ratio_se_n": "NA",
    "mean_diff": "NaN",
    "ratio_diff": "NaN",
    "sd_y_est1": "NaN",
    "sd_y_est2": "NA",
    "r2_sum1": 0,
    "r2_sum2": 0,
    "r2_sum3": 0,
    "r2_sum4": 0,
    "ldsc_nsnp_merge_refpanel_ld": 1288991,
    "ldsc_nsnp_merge_regression_ld": 1288991,
    "ldsc_observed_scale_h2_beta": 0.0422,
    "ldsc_observed_scale_h2_se": 0.0024,
    "ldsc_intercept_beta": 1.0326,
    "ldsc_intercept_se": 0.0075,
    "ldsc_lambda_gc": 1.2493,
    "ldsc_mean_chisq": 1.29,
    "ldsc_ratio": 0.1124
}

Flags

name value
af_correlation FALSE
inflation_factor FALSE
n TRUE
is_snpid_non_unique FALSE
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 logical.mean logical.count numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
character ID 0 1.0000000 3 58 0 9769847 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.0000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 9783807 0.0000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.0000000 NA NA NA NA NA NA NA 8.623293e+00 5.748977e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.885831e+07 5.629193e+07 828.0000000 3.258020e+07 6.947516e+07 1.145937e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.162000e-04 8.882500e-03 -0.1614730 -2.917700e-03 -3.120000e-05 2.823400e-03 1.359640e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 6.293500e-03 5.807900e-03 0.0018054 2.206600e-03 3.671500e-03 8.402600e-03 9.575280e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.744860e-01 2.963931e-01 0.0000000 2.099999e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.744875e-01 2.963687e-01 0.0000000 2.110800e-01 4.653217e-01 7.321668e-01 9.999999e-01 ▇▆▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.046758e-01 2.569294e-01 0.0011280 1.364200e-02 7.962200e-02 3.186910e-01 9.988720e-01 ▇▂▁▁▁
numeric AF_reference 175266 0.9820861 NA NA NA NA NA NA NA 2.076836e-01 2.484348e-01 0.0000000 1.218050e-02 1.012380e-01 3.220850e-01 1.000000e+00 ▇▂▁▁▁

Head and tail

CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
1 49298 rs200943160 T C -0.0005656 0.0033222 0.8600001 0.8648104 0.623465 0.7821490 NA
1 54676 rs2462492 C T 0.0015062 0.0032962 0.6499995 0.6476981 0.400383 NA NA
1 86028 rs114608975 T C -0.0014901 0.0052519 0.7800007 0.7766169 0.103642 0.0277556 NA
1 91536 rs6702460 G T 0.0032407 0.0032395 0.3200000 0.3171253 0.456490 0.4207270 NA
1 234313 rs8179466 C T -0.0031690 0.0063793 0.6200004 0.6193540 0.074513 NA NA
1 534192 rs6680723 C T -0.0015939 0.0037016 0.6700003 0.6667562 0.240901 NA NA
1 546697 rs12025928 A G -0.0009725 0.0046241 0.8300000 0.8334267 0.913514 NA NA
1 693731 rs12238997 A G -0.0010076 0.0031020 0.7499995 0.7453047 0.116289 0.1417730 NA
1 705882 rs72631875 G A -0.0003577 0.0045647 0.9400001 0.9375382 0.066943 0.0315495 NA
1 706368 rs55727773 A G 0.0024288 0.0022974 0.2900000 0.2904326 0.515463 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0013288 0.0048268 0.7800007 0.7830821 0.041986 0.0473243 NA
22 51219766 rs182321900 C T -0.0210535 0.0227903 0.3599996 0.3555945 0.001895 NA NA
22 51220146 rs868950473 C T -0.0266148 0.0225758 0.2399999 0.2384347 0.001942 NA NA
22 51221190 rs369304721 G A 0.0014968 0.0048212 0.7600007 0.7562037 0.049814 NA NA
22 51221731 rs115055839 T C -0.0018991 0.0036013 0.5999997 0.5979536 0.073397 0.0625000 NA
22 51222100 rs114553188 G T -0.0082088 0.0042455 0.0530005 0.0531690 0.054389 0.0880591 NA
22 51223637 rs375798137 G A -0.0079352 0.0042656 0.0629999 0.0628438 0.054022 0.0788738 NA
22 51229805 rs9616985 T C -0.0016463 0.0036137 0.6499995 0.6487017 0.073240 0.0730831 NA
22 51232488 rs376461333 A G -0.0075346 0.0085536 0.3800004 0.3783885 0.019934 NA NA
22 51237063 rs3896457 T C 0.0018099 0.0022140 0.4100001 0.4136612 0.297985 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623465 ES:SE:LP:AF:ID  -0.000565621:0.00332222:0.0655015:0.623465:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400383 ES:SE:LP:AF:ID  0.00150623:0.00329618:0.187087:0.400383:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103642 ES:SE:LP:AF:ID  -0.00149013:0.00525192:0.107905:0.103642:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.45649  ES:SE:LP:AF:ID  0.0032407:0.00323946:0.49485:0.45649:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074513 ES:SE:LP:AF:ID  -0.00316902:0.0063793:0.207608:0.074513:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240901 ES:SE:LP:AF:ID  -0.00159392:0.00370159:0.173925:0.240901:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913514 ES:SE:LP:AF:ID  -0.000972493:0.00462412:0.0809219:0.913514:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116289 ES:SE:LP:AF:ID  -0.00100764:0.00310199:0.124939:0.116289:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.066943 ES:SE:LP:AF:ID  -0.000357712:0.00456473:0.0268721:0.066943:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515463 ES:SE:LP:AF:ID  0.00242876:0.00229741:0.537602:0.515463:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.03294  ES:SE:LP:AF:ID  0.000628726:0.00580187:0.0409586:0.03294:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036557 ES:SE:LP:AF:ID  0.00151632:0.00526853:0.113509:0.036557:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036675 ES:SE:LP:AF:ID  0.00106781:0.00524861:0.0757207:0.036675:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036374 ES:SE:LP:AF:ID  0.000674792:0.00528627:0.0457575:0.036374:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016391 ES:SE:LP:AF:ID  -0.000487725:0.00813791:0.0222764:0.016391:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036904 ES:SE:LP:AF:ID  0.00101022:0.00522815:0.0705811:0.036904:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037004 ES:SE:LP:AF:ID  0.000972407:0.00521021:0.0705811:0.037004:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101065 ES:SE:LP:AF:ID  -0.000764622:0.00379086:0.0757207:0.101065:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959179 ES:SE:LP:AF:ID  -0.00106901:0.00502578:0.0809219:0.959179:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031501 ES:SE:LP:AF:ID  -0.00017094:0.00907516:0.00877392:0.031501:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.05315  ES:SE:LP:AF:ID  -0.0022559:0.00726167:0.119186:0.05315:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036536 ES:SE:LP:AF:ID  0.00114497:0.00524355:0.0809219:0.036536:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036829 ES:SE:LP:AF:ID  0.00121144:0.00519738:0.0861861:0.036829:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843322 ES:SE:LP:AF:ID  0.00152485:0.00268717:0.244125:0.843322:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055819 ES:SE:LP:AF:ID  -0.000807394:0.00435737:0.0705811:0.055819:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122291 ES:SE:LP:AF:ID  -0.00196234:0.0029416:0.30103:0.122291:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025873 ES:SE:LP:AF:ID  0.0126948:0.00721063:1.10791:0.025873:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121534 ES:SE:LP:AF:ID  -0.00173203:0.0029428:0.251812:0.121534:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13226  ES:SE:LP:AF:ID  -0.00179363:0.00290089:0.267606:0.13226:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011165 ES:SE:LP:AF:ID  -0.00359421:0.0105433:0.136677:0.011165:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005685 ES:SE:LP:AF:ID  0.0147205:0.0136524:0.552842:0.005685:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002281 ES:SE:LP:AF:ID  -0.000851507:0.022865:0.0132283:0.002281:rs112573343
1   752478  rs146277091 G   A   .   PASS    AF=0.036775 ES:SE:LP:AF:ID  0.000842698:0.00514295:0.0604807:0.036775:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839009 ES:SE:LP:AF:ID  0.00134835:0.00260261:0.221849:0.839009:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838645 ES:SE:LP:AF:ID  0.00127963:0.0025998:0.207608:0.838645:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869775 ES:SE:LP:AF:ID  0.00144789:0.00278997:0.221849:0.869775:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.12987  ES:SE:LP:AF:ID  -0.00128045:0.00279566:0.187087:0.12987:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.03724  ES:SE:LP:AF:ID  0.000712628:0.00505821:0.05061:0.03724:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037484 ES:SE:LP:AF:ID  0.000849165:0.00502614:0.0604807:0.037484:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869113 ES:SE:LP:AF:ID  0.00138005:0.00278442:0.207608:0.869113:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869218 ES:SE:LP:AF:ID  0.00140123:0.00278564:0.21467:0.869218:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037448 ES:SE:LP:AF:ID  0.000145924:0.00504788:0.00877392:0.037448:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869115 ES:SE:LP:AF:ID  0.00138111:0.00278437:0.207608:0.869115:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005161 ES:SE:LP:AF:ID  0.0128253:0.0142619:0.431798:0.005161:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005128 ES:SE:LP:AF:ID  0.0131283:0.0142984:0.443698:0.005128:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.83809  ES:SE:LP:AF:ID  0.0014702:0.0025925:0.244125:0.83809:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037468 ES:SE:LP:AF:ID  0.000194911:0.00505455:0.0132283:0.037468:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838709 ES:SE:LP:AF:ID  0.00130276:0.00259952:0.207608:0.838709:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013686 ES:SE:LP:AF:ID  0.00615664:0.0091159:0.30103:0.013686:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005594 ES:SE:LP:AF:ID  0.017073:0.0139577:0.657577:0.005594:rs184270342