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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    "FORMAT.5": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
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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_3496.vcf.gz --id UKB-b:3709 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3496.txt.gz --cohort_controls 35536 --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-3709/UKB-b-3709_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3709/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:45 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-3709/UKB-b-3709_data.vcf.gz ...
Read summary statistics for 7756912 SNPs.
Dropped 5648 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, 1280038 SNPs remain.
After merging with regression SNP LD, 1280038 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0716 (0.0137)
Lambda GC: 1.0691
Mean Chi^2: 1.0649
Intercept: 1.0148 (0.0063)
Ratio: 0.2281 (0.0966)
Analysis finished at Thu Oct 17 14:45:19 2019
Total time elapsed: 1.0m:33.95s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9417,
    "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": 72262,
    "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": 1280038,
    "ldsc_nsnp_merge_regression_ld": 1280038,
    "ldsc_observed_scale_h2_beta": 0.0716,
    "ldsc_observed_scale_h2_se": 0.0137,
    "ldsc_intercept_beta": 1.0148,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.0691,
    "ldsc_mean_chisq": 1.0649,
    "ldsc_ratio": 0.228
}

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 7751290 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 7756912 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 8.662149e+00 5.764371e+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 7.868427e+07 5.643394e+07 828.0000000 3.221539e+07 6.914071e+07 1.145600e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -4.070000e-05 1.540290e-02 -0.1569570 -7.494500e-03 -3.890000e-05 7.418000e-03 1.492200e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.317680e-02 7.500300e-03 0.0062666 7.253600e-03 9.895500e-03 1.715540e-02 7.703190e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.907463e-01 2.909882e-01 0.0000004 2.399999e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.907473e-01 2.909601e-01 0.0000004 2.363457e-01 4.874274e-01 7.428129e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.524436e-01 2.608140e-01 0.0098500 4.033000e-02 1.464830e-01 3.992950e-01 9.901500e-01 ▇▂▂▁▁
numeric AF_reference 72262 0.9906842 NA NA NA NA NA NA NA 2.515376e-01 2.526811e-01 0.0000000 4.392970e-02 1.611420e-01 3.939700e-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 -0.0007776 0.0116272 0.9500000 0.9466795 0.622689 0.7821490 NA
1 54676 rs2462492 C T 0.0138157 0.0114884 0.2300001 0.2291396 0.400038 NA NA
1 86028 rs114608975 T C -0.0236687 0.0185352 0.2000000 0.2016165 0.103147 0.0277556 NA
1 91536 rs6702460 G T -0.0015290 0.0113551 0.8900000 0.8928826 0.456499 0.4207270 NA
1 234313 rs8179466 C T 0.0114912 0.0224780 0.6100002 0.6091971 0.073983 NA NA
1 534192 rs6680723 C T 0.0017479 0.0128030 0.8900000 0.8914081 0.243800 NA NA
1 546697 rs12025928 A G -0.0126185 0.0163717 0.4400003 0.4408547 0.914819 NA NA
1 693731 rs12238997 A G -0.0035180 0.0108104 0.7400005 0.7448555 0.116281 0.1417730 NA
1 705882 rs72631875 G A 0.0220004 0.0160327 0.1700000 0.1699948 0.066299 0.0315495 NA
1 706368 rs55727773 A G -0.0080256 0.0080307 0.3200000 0.3176216 0.513169 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0032100 0.0097926 0.7400005 0.7430678 0.135139 0.2052720 NA
22 51219387 rs9616832 T C 0.0017666 0.0127189 0.8900000 0.8895307 0.071651 0.0654952 NA
22 51219704 rs147475742 G A 0.0049229 0.0170269 0.7700005 0.7724845 0.040880 0.0473243 NA
22 51221190 rs369304721 G A -0.0015373 0.0170542 0.9299999 0.9281739 0.048117 NA NA
22 51221731 rs115055839 T C 0.0019298 0.0127235 0.8800001 0.8794456 0.071248 0.0625000 NA
22 51222100 rs114553188 G T 0.0065889 0.0149564 0.6600001 0.6595471 0.053747 0.0880591 NA
22 51223637 rs375798137 G A 0.0058710 0.0150240 0.6999999 0.6959652 0.053379 0.0788738 NA
22 51229805 rs9616985 T C 0.0019641 0.0127585 0.8800001 0.8776523 0.071118 0.0730831 NA
22 51232488 rs376461333 A G 0.0206971 0.0306114 0.5000000 0.4989620 0.019230 NA NA
22 51237063 rs3896457 T C -0.0052630 0.0077090 0.4899999 0.4947924 0.297300 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.622689 ES:SE:LP:AF:ID  -0.000777594:0.0116272:0.0222764:0.622689:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400038 ES:SE:LP:AF:ID  0.0138157:0.0114884:0.638272:0.400038:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103147 ES:SE:LP:AF:ID  -0.0236687:0.0185352:0.69897:0.103147:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456499 ES:SE:LP:AF:ID  -0.00152905:0.0113551:0.05061:0.456499:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.073983 ES:SE:LP:AF:ID  0.0114912:0.022478:0.21467:0.073983:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.2438   ES:SE:LP:AF:ID  0.0017479:0.012803:0.05061:0.2438:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.914819 ES:SE:LP:AF:ID  -0.0126185:0.0163717:0.356547:0.914819:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116281 ES:SE:LP:AF:ID  -0.00351803:0.0108104:0.130768:0.116281:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.066299 ES:SE:LP:AF:ID  0.0220004:0.0160327:0.769551:0.066299:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513169 ES:SE:LP:AF:ID  -0.00802556:0.00803072:0.49485:0.513169:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.03361  ES:SE:LP:AF:ID  0.0360398:0.0200597:1.14267:0.03361:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037283 ES:SE:LP:AF:ID  0.034297:0.0182047:1.22185:0.037283:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037342 ES:SE:LP:AF:ID  0.0336497:0.0181572:1.19382:0.037342:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037073 ES:SE:LP:AF:ID  0.0349932:0.0182734:1.25964:0.037073:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016657 ES:SE:LP:AF:ID  -0.0246728:0.0281117:0.420216:0.016657:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03765  ES:SE:LP:AF:ID  0.0335569:0.0180638:1.20066:0.03765:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037758 ES:SE:LP:AF:ID  0.0317045:0.0179918:1.10791:0.037758:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.099537 ES:SE:LP:AF:ID  0.00845694:0.0133408:0.275724:0.099537:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958188 ES:SE:LP:AF:ID  -0.0230541:0.0172858:0.744727:0.958188:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.030833 ES:SE:LP:AF:ID  -0.0251871:0.0326541:0.356547:0.030833:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053375 ES:SE:LP:AF:ID  0.00143723:0.0253418:0.0222764:0.053375:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037325 ES:SE:LP:AF:ID  0.0317921:0.0181093:1.10237:0.037325:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037555 ES:SE:LP:AF:ID  0.034522:0.0179695:1.25964:0.037555:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842092 ES:SE:LP:AF:ID  -0.00618918:0.00932949:0.29243:0.842092:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056941 ES:SE:LP:AF:ID  -0.00443252:0.0150962:0.113509:0.056941:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123015 ES:SE:LP:AF:ID  0.000150446:0.0102182:0.00436481:0.123015:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025782 ES:SE:LP:AF:ID  -0.0387377:0.0253938:0.886057:0.025782:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122237 ES:SE:LP:AF:ID  1.44695e-05:0.0102232:-0:0.122237:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.13287  ES:SE:LP:AF:ID  0.0115797:0.0101043:0.60206:0.13287:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011754 ES:SE:LP:AF:ID  -0.0365531:0.0359923:0.508638:0.011754:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.037599 ES:SE:LP:AF:ID  0.0310672:0.017757:1.09691:0.037599:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838086 ES:SE:LP:AF:ID  -0.00547583:0.00905467:0.259637:0.838086:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.837677 ES:SE:LP:AF:ID  -0.00510489:0.00904098:0.244125:0.837677:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869195 ES:SE:LP:AF:ID  -7.37008e-05:0.00971444:0.00436481:0.869195:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130672 ES:SE:LP:AF:ID  -0.000505508:0.00972409:0.0177288:0.130672:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038004 ES:SE:LP:AF:ID  0.0312474:0.0174835:1.13077:0.038004:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038245 ES:SE:LP:AF:ID  0.0282958:0.0173829:1:0.038245:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868568 ES:SE:LP:AF:ID  0.000547381:0.00969373:0.0222764:0.868568:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868657 ES:SE:LP:AF:ID  0.000404852:0.00969627:0.0132283:0.868657:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038217 ES:SE:LP:AF:ID  0.0297431:0.0174417:1.05552:0.038217:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868581 ES:SE:LP:AF:ID  0.000488296:0.00969327:0.0177288:0.868581:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.837186 ES:SE:LP:AF:ID  -0.00443462:0.00902233:0.207608:0.837186:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038226 ES:SE:LP:AF:ID  0.0287171:0.017467:1:0.038226:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.837835 ES:SE:LP:AF:ID  -0.00434193:0.00904764:0.200659:0.837835:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013786 ES:SE:LP:AF:ID  -0.046159:0.031881:0.823909:0.013786:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.839049 ES:SE:LP:AF:ID  -0.00328265:0.00917227:0.142668:0.839049:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868808 ES:SE:LP:AF:ID  0.000751394:0.00968257:0.0268721:0.868808:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868337 ES:SE:LP:AF:ID  0.000631358:0.00965942:0.0222764:0.868337:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867268 ES:SE:LP:AF:ID  0.00125546:0.00964165:0.0457575:0.867268:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868534 ES:SE:LP:AF:ID  0.000684613:0.00966825:0.0268721:0.868534:rs4951929