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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    "gwas_harmonisation_command": "--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/vcf_09_19b/bgzip_vcf/data.batch_2684.vcf.gz --id UKB-b:3016 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2684.txt.gz --cohort_controls 149403 --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-3016/UKB-b-3016_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3016/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:12 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3016/UKB-b-3016_data.vcf.gz ...
Read summary statistics for 9306354 SNPs.
Dropped 10325 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, 1287945 SNPs remain.
After merging with regression SNP LD, 1287945 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0073 (0.0029)
Lambda GC: 1.0359
Mean Chi^2: 1.0336
Intercept: 1.012 (0.0057)
Ratio: 0.3564 (0.171)
Analysis finished at Thu Oct 17 14:44:57 2019
Total time elapsed: 1.0m:45.4s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9486,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 0,
    "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": 112966,
    "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": 1287945,
    "ldsc_nsnp_merge_regression_ld": 1287945,
    "ldsc_observed_scale_h2_beta": 0.0073,
    "ldsc_observed_scale_h2_se": 0.0029,
    "ldsc_intercept_beta": 1.012,
    "ldsc_intercept_se": 0.0057,
    "ldsc_lambda_gc": 1.0359,
    "ldsc_mean_chisq": 1.0336,
    "ldsc_ratio": 0.3571
}

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 9296082 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 9306354 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 8.634390e+00 5.754011e+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 7.881196e+07 5.631102e+07 828.0000000 3.250138e+07 6.939165e+07 1.145441e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.430000e-05 7.030000e-03 -0.0902168 -2.524500e-03 9.700000e-06 2.564500e-03 8.952660e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.474500e-03 4.328400e-03 0.0018331 2.207400e-03 3.487300e-03 7.500900e-03 6.439100e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.954699e-01 2.896828e-01 0.0000013 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 4.954705e-01 2.896556e-01 0.0000013 2.438645e-01 4.934254e-01 7.463763e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.137156e-01 2.577887e-01 0.0023430 1.747200e-02 9.239800e-02 3.356080e-01 9.976570e-01 ▇▂▁▁▁
numeric AF_reference 112966 0.9878614 NA NA NA NA NA NA NA 2.147047e-01 2.495913e-01 0.0000000 1.477640e-02 1.108230e-01 3.354630e-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 -0.0043779 0.0033766 0.1900002 0.1947927 0.622996 0.7821490 NA
1 54676 rs2462492 C T -0.0043214 0.0033419 0.2000000 0.1959780 0.401245 NA NA
1 86028 rs114608975 T C 0.0028519 0.0053453 0.5900000 0.5936572 0.103504 0.0277556 NA
1 91536 rs6702460 G T -0.0073946 0.0032890 0.0250000 0.0245573 0.457346 0.4207270 NA
1 234313 rs8179466 C T -0.0053348 0.0065090 0.4100001 0.4124405 0.074405 NA NA
1 534192 rs6680723 C T 0.0003032 0.0037611 0.9400001 0.9357451 0.241491 NA NA
1 546697 rs12025928 A G -0.0028484 0.0046828 0.5400003 0.5430111 0.913517 NA NA
1 693731 rs12238997 A G -0.0012917 0.0031421 0.6800001 0.6810145 0.117152 0.1417730 NA
1 705882 rs72631875 G A -0.0070900 0.0046281 0.1299999 0.1255382 0.066930 0.0315495 NA
1 706368 rs55727773 A G 0.0031206 0.0023322 0.1800002 0.1808839 0.515005 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0014554 0.0028228 0.6100002 0.6061402 0.137750 0.2052720 NA
22 51219387 rs9616832 T C -0.0034649 0.0036680 0.3400001 0.3448563 0.073282 0.0654952 NA
22 51219704 rs147475742 G A 0.0004898 0.0049262 0.9199999 0.9208025 0.041557 0.0473243 NA
22 51221190 rs369304721 G A 0.0005410 0.0048983 0.9100000 0.9120523 0.049479 NA NA
22 51221731 rs115055839 T C -0.0034281 0.0036689 0.3500000 0.3501076 0.072826 0.0625000 NA
22 51222100 rs114553188 G T -0.0000722 0.0042927 0.9900000 0.9865797 0.054793 0.0880591 NA
22 51223637 rs375798137 G A -0.0003835 0.0043143 0.9299999 0.9291670 0.054409 0.0788738 NA
22 51229805 rs9616985 T C -0.0034917 0.0036815 0.3400001 0.3429016 0.072708 0.0730831 NA
22 51232488 rs376461333 A G 0.0032237 0.0086864 0.7099994 0.7105491 0.019972 NA NA
22 51237063 rs3896457 T C -0.0002331 0.0022416 0.9199999 0.9171631 0.298965 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.622996 ES:SE:LP:AF:ID  -0.00437793:0.00337665:0.721246:0.622996:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.401245 ES:SE:LP:AF:ID  -0.00432137:0.00334188:0.69897:0.401245:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103504 ES:SE:LP:AF:ID  0.00285193:0.00534526:0.229148:0.103504:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457346 ES:SE:LP:AF:ID  -0.00739458:0.00328897:1.60206:0.457346:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074405 ES:SE:LP:AF:ID  -0.00533481:0.00650899:0.387216:0.074405:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241491 ES:SE:LP:AF:ID  0.000303218:0.00376113:0.0268721:0.241491:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913517 ES:SE:LP:AF:ID  -0.0028484:0.00468282:0.267606:0.913517:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117152 ES:SE:LP:AF:ID  -0.00129166:0.00314212:0.167491:0.117152:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.06693  ES:SE:LP:AF:ID  -0.00709:0.00462813:0.886057:0.06693:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515005 ES:SE:LP:AF:ID  0.0031206:0.00233222:0.744727:0.515005:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032703 ES:SE:LP:AF:ID  0.00607998:0.00590851:0.522879:0.032703:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036231 ES:SE:LP:AF:ID  0.00643265:0.00537497:0.638272:0.036231:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036321 ES:SE:LP:AF:ID  0.00576185:0.00535652:0.552842:0.036321:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036006 ES:SE:LP:AF:ID  0.00645624:0.00539707:0.638272:0.036006:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016478 ES:SE:LP:AF:ID  0.00139011:0.00822213:0.0604807:0.016478:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036543 ES:SE:LP:AF:ID  0.00651797:0.0053376:0.657577:0.036543:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036621 ES:SE:LP:AF:ID  0.00642804:0.0053199:0.638272:0.036621:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.10124  ES:SE:LP:AF:ID  -0.00546655:0.00384935:0.79588:0.10124:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959752 ES:SE:LP:AF:ID  -0.00589814:0.0051424:0.60206:0.959752:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031333 ES:SE:LP:AF:ID  -0.00121031:0.00925699:0.0457575:0.031333:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053537 ES:SE:LP:AF:ID  0.000635552:0.0073333:0.0315171:0.053537:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036201 ES:SE:LP:AF:ID  0.00634341:0.00534821:0.619789:0.036201:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036544 ES:SE:LP:AF:ID  0.0072666:0.00529975:0.769551:0.036544:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842801 ES:SE:LP:AF:ID  -0.00116182:0.00272921:0.173925:0.842801:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056074 ES:SE:LP:AF:ID  0.00203659:0.00440865:0.19382:0.056074:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123246 ES:SE:LP:AF:ID  -0.00134412:0.00297943:0.187087:0.123246:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.02574  ES:SE:LP:AF:ID  0.00129467:0.00734775:0.0655015:0.02574:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122486 ES:SE:LP:AF:ID  -0.00135645:0.00298034:0.187087:0.122486:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13289  ES:SE:LP:AF:ID  0.000480533:0.00294166:0.0604807:0.13289:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.01099  ES:SE:LP:AF:ID  0.00733927:0.0107849:0.30103:0.01099:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005897 ES:SE:LP:AF:ID  -0.013412:0.0135364:0.49485:0.005897:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.036433 ES:SE:LP:AF:ID  0.00641037:0.00524712:0.657577:0.036433:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838333 ES:SE:LP:AF:ID  -0.000547417:0.00264047:0.0757207:0.838333:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837976 ES:SE:LP:AF:ID  -0.000466675:0.0026379:0.0655015:0.837976:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868915 ES:SE:LP:AF:ID  0.0012459:0.00282618:0.180456:0.868915:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130765 ES:SE:LP:AF:ID  -0.00129677:0.00283235:0.187087:0.130765:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.036875 ES:SE:LP:AF:ID  0.00578726:0.00516218:0.585027:0.036875:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037138 ES:SE:LP:AF:ID  0.00528827:0.0051274:0.522879:0.037138:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868269 ES:SE:LP:AF:ID  0.00128416:0.00282119:0.187087:0.868269:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868342 ES:SE:LP:AF:ID  0.00131628:0.00282217:0.19382:0.868342:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037072 ES:SE:LP:AF:ID  0.00545478:0.00515082:0.537602:0.037072:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868264 ES:SE:LP:AF:ID  0.00131814:0.00282102:0.19382:0.868264:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005124 ES:SE:LP:AF:ID  -0.00287315:0.0144914:0.0757207:0.005124:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005093 ES:SE:LP:AF:ID  -0.00292596:0.0145242:0.0757207:0.005093:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837453 ES:SE:LP:AF:ID  -0.000487198:0.00263066:0.0705811:0.837453:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037077 ES:SE:LP:AF:ID  0.00579494:0.00515898:0.585027:0.037077:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.83809  ES:SE:LP:AF:ID  -0.000696598:0.0026382:0.102373:0.83809:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013475 ES:SE:LP:AF:ID  0.0175098:0.00932835:1.21467:0.013475:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005697 ES:SE:LP:AF:ID  0.00242256:0.013997:0.0655015:0.005697:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.839274 ES:SE:LP:AF:ID  -0.000735051:0.00267381:0.107905:0.839274:rs3131965