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_5160.vcf.gz --id UKB-b:6251 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_5160.txt.gz --cohort_controls 94284 --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-6251/UKB-b-6251_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6251/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6251/UKB-b-6251_data.vcf.gz ...
Read summary statistics for 8978754 SNPs.
Dropped 8614 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, 1287136 SNPs remain.
After merging with regression SNP LD, 1287136 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0366 (0.0051)
Lambda GC: 1.0564
Mean Chi^2: 1.0723
Intercept: 1.003 (0.0068)
Ratio: 0.0409 (0.0946)
Analysis finished at Thu Oct 17 14:42:05 2019
Total time elapsed: 1.0m:45.91s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9478,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 8.1683e-06,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 0,
    "n_p_sig": 0,
    "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": 92381,
    "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": 1287136,
    "ldsc_nsnp_merge_regression_ld": 1287136,
    "ldsc_observed_scale_h2_beta": 0.0366,
    "ldsc_observed_scale_h2_se": 0.0051,
    "ldsc_intercept_beta": 1.003,
    "ldsc_intercept_se": 0.0068,
    "ldsc_lambda_gc": 1.0564,
    "ldsc_mean_chisq": 1.0723,
    "ldsc_ratio": 0.0415
}

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 8970179 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 8978754 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 8.643646e+00 5.758327e+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 7.878910e+07 5.634224e+07 828.0000000 3.242766e+07 6.935282e+07 1.145509e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 8.200000e-06 1.503220e-02 -0.1626960 -5.912200e-03 2.560000e-05 5.956200e-03 1.844780e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.206060e-02 8.783700e-03 0.0044145 5.257800e-03 8.027200e-03 1.653200e-02 1.025800e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.934995e-01 2.911741e-01 0.0000004 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.934989e-01 2.911480e-01 0.0000004 2.394448e-01 4.917393e-01 7.460446e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.208614e-01 2.586129e-01 0.0037130 2.083400e-02 1.024120e-01 3.483650e-01 9.962870e-01 ▇▂▁▁▁
numeric AF_reference 92381 0.9897112 NA NA NA NA NA NA NA 2.211196e-01 2.505271e-01 0.0000000 1.817090e-02 1.198080e-01 3.466450e-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 -0.0137556 0.0081536 0.0920005 0.0915929 0.623519 0.7821490 NA
1 54676 rs2462492 C T 0.0070961 0.0081010 0.3800004 0.3810535 0.398861 NA NA
1 86028 rs114608975 T C 0.0312831 0.0128264 0.0150000 0.0147295 0.104165 0.0277556 NA
1 91536 rs6702460 G T 0.0117834 0.0079545 0.1400000 0.1385148 0.455506 0.4207270 NA
1 234313 rs8179466 C T -0.0146262 0.0155857 0.3500000 0.3480198 0.074792 NA NA
1 534192 rs6680723 C T -0.0013955 0.0091051 0.8800001 0.8781848 0.240652 NA NA
1 546697 rs12025928 A G 0.0219598 0.0112868 0.0519996 0.0517007 0.912799 NA NA
1 693731 rs12238997 A G -0.0178602 0.0075636 0.0179999 0.0182089 0.117981 0.1417730 NA
1 705882 rs72631875 G A -0.0025412 0.0110917 0.8200001 0.8187869 0.067587 0.0315495 NA
1 706368 rs55727773 A G 0.0066762 0.0056148 0.2300001 0.2344195 0.514345 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0099436 0.0067949 0.1400000 0.1433563 0.137218 0.2052720 NA
22 51219387 rs9616832 T C 0.0017057 0.0088550 0.8499999 0.8472522 0.072666 0.0654952 NA
22 51219704 rs147475742 G A 0.0074212 0.0117817 0.5300002 0.5287668 0.041803 0.0473243 NA
22 51221190 rs369304721 G A -0.0010113 0.0118417 0.9299999 0.9319421 0.049165 NA NA
22 51221731 rs115055839 T C 0.0010671 0.0088573 0.9000000 0.9041024 0.072197 0.0625000 NA
22 51222100 rs114553188 G T -0.0263447 0.0103701 0.0109999 0.0110711 0.054414 0.0880591 NA
22 51223637 rs375798137 G A -0.0262461 0.0104249 0.0120000 0.0118145 0.054025 0.0788738 NA
22 51229805 rs9616985 T C 0.0008025 0.0088892 0.9299999 0.9280698 0.072064 0.0730831 NA
22 51232488 rs376461333 A G -0.0479359 0.0209602 0.0219999 0.0221960 0.020027 NA NA
22 51237063 rs3896457 T C 0.0084102 0.0054047 0.1199999 0.1196905 0.298597 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623519 ES:SE:LP:AF:ID  -0.0137556:0.00815362:1.03621:0.623519:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.398861 ES:SE:LP:AF:ID  0.0070961:0.00810097:0.420216:0.398861:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.104165 ES:SE:LP:AF:ID  0.0312831:0.0128264:1.82391:0.104165:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455506 ES:SE:LP:AF:ID  0.0117834:0.00795453:0.853872:0.455506:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074792 ES:SE:LP:AF:ID  -0.0146262:0.0155857:0.455932:0.074792:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240652 ES:SE:LP:AF:ID  -0.00139554:0.00910507:0.0555173:0.240652:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912799 ES:SE:LP:AF:ID  0.0219598:0.0112868:1.284:0.912799:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117981 ES:SE:LP:AF:ID  -0.0178602:0.00756358:1.74473:0.117981:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067587 ES:SE:LP:AF:ID  -0.00254117:0.0110917:0.0861861:0.067587:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514345 ES:SE:LP:AF:ID  0.00667625:0.00561477:0.638272:0.514345:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033593 ES:SE:LP:AF:ID  -0.00204157:0.0140324:0.0555173:0.033593:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.03724  ES:SE:LP:AF:ID  -0.00426117:0.0127615:0.130768:0.03724:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037327 ES:SE:LP:AF:ID  -0.00406998:0.0127195:0.124939:0.037327:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037028 ES:SE:LP:AF:ID  -0.00458086:0.012806:0.142668:0.037028:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016786 ES:SE:LP:AF:ID  -0.0419505:0.0197025:1.48149:0.016786:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03759  ES:SE:LP:AF:ID  -0.00387656:0.012664:0.119186:0.03759:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037684 ES:SE:LP:AF:ID  -0.00307219:0.0126233:0.091515:0.037684:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101099 ES:SE:LP:AF:ID  0.00722634:0.00929505:0.356547:0.101099:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.95811  ES:SE:LP:AF:ID  0.00128587:0.0121407:0.0362122:0.95811:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031879 ES:SE:LP:AF:ID  -0.0317054:0.0221177:0.823909:0.031879:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052624 ES:SE:LP:AF:ID  0.00611567:0.0178864:0.136677:0.052624:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037162 ES:SE:LP:AF:ID  -0.00330049:0.012708:0.09691:0.037162:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037473 ES:SE:LP:AF:ID  -0.00353549:0.0126011:0.107905:0.037473:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.84083  ES:SE:LP:AF:ID  0.0157666:0.00655559:1.79588:0.84083:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056201 ES:SE:LP:AF:ID  -0.0114997:0.0106677:0.552842:0.056201:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123802 ES:SE:LP:AF:ID  -0.0172361:0.00718465:1.79588:0.123802:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025858 ES:SE:LP:AF:ID  -0.000702216:0.0176584:0.0132283:0.025858:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122996 ES:SE:LP:AF:ID  -0.0176543:0.00718832:1.85387:0.122996:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.133705 ES:SE:LP:AF:ID  -0.0139049:0.00708775:1.30103:0.133705:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011257 ES:SE:LP:AF:ID  0.0195768:0.0256832:0.346787:0.011257:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006102 ES:SE:LP:AF:ID  -0.0244274:0.0320881:0.346787:0.006102:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.03744  ES:SE:LP:AF:ID  -0.00262306:0.0124627:0.0809219:0.03744:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.836684 ES:SE:LP:AF:ID  0.0151156:0.00634566:1.76955:0.836684:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836239 ES:SE:LP:AF:ID  0.0150547:0.0063381:1.74473:0.836239:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.867952 ES:SE:LP:AF:ID  0.0161957:0.00679905:1.76955:0.867952:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.1317   ES:SE:LP:AF:ID  -0.0158586:0.00681333:1.69897:0.1317:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037883 ES:SE:LP:AF:ID  -0.00380379:0.012266:0.119186:0.037883:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038129 ES:SE:LP:AF:ID  -0.00381097:0.0121901:0.124939:0.038129:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867249 ES:SE:LP:AF:ID  0.0161718:0.00678531:1.76955:0.867249:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.867349 ES:SE:LP:AF:ID  0.0159567:0.00678857:1.72125:0.867349:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038075 ES:SE:LP:AF:ID  -0.00312715:0.0122394:0.09691:0.038075:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867247 ES:SE:LP:AF:ID  0.0163396:0.00678488:1.79588:0.867247:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005076 ES:SE:LP:AF:ID  -0.0102577:0.0352541:0.113509:0.005076:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005043 ES:SE:LP:AF:ID  -0.00890294:0.0353523:0.09691:0.005043:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.835776 ES:SE:LP:AF:ID  0.0154039:0.00632455:1.82391:0.835776:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038092 ES:SE:LP:AF:ID  -0.00224518:0.0122563:0.0705811:0.038092:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836385 ES:SE:LP:AF:ID  0.0154494:0.00634173:1.82391:0.836385:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013144 ES:SE:LP:AF:ID  -0.0109227:0.0228047:0.200659:0.013144:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.00552  ES:SE:LP:AF:ID  0.0109224:0.0343757:0.124939:0.00552:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.837694 ES:SE:LP:AF:ID  0.0151045:0.00642866:1.72125:0.837694:rs3131965