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_2207.vcf.gz --id UKB-b:14576 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2207.txt.gz --cohort_cases 411268 --cohort_controls 50986 --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-14576/UKB-b-14576_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14576/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:44 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-14576/UKB-b-14576_data.vcf.gz ...
Read summary statistics for 9851866 SNPs.
Dropped 14738 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, 1289166 SNPs remain.
After merging with regression SNP LD, 1289166 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0151 (0.0012)
Lambda GC: 1.1787
Mean Chi^2: 1.1879
Intercept: 1.0498 (0.0069)
Ratio: 0.2652 (0.0365)
Analysis finished at Thu Oct 17 14:45:04 2019
Total time elapsed: 1.0m:20.21s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9499,
    "inflation_factor": 1.1474,
    "mean_EFFECT": -0.0001,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 2,
    "n_p_sig": 29,
    "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": 184849,
    "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": 1289166,
    "ldsc_nsnp_merge_regression_ld": 1289166,
    "ldsc_observed_scale_h2_beta": 0.0151,
    "ldsc_observed_scale_h2_se": 0.0012,
    "ldsc_intercept_beta": 1.0498,
    "ldsc_intercept_se": 0.0069,
    "ldsc_lambda_gc": 1.1787,
    "ldsc_mean_chisq": 1.1879,
    "ldsc_ratio": 0.265
}

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 9837196 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 9851866 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 8.622825e+00 5.748290e+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 7.886027e+07 5.628334e+07 828.0000000 3.259061e+07 6.948835e+07 1.145912e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.139000e-04 3.287900e-03 -0.0803305 -1.022800e-03 -2.510000e-05 9.392000e-04 5.390070e-02 ▁▁▇▆▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.242200e-03 2.122400e-03 0.0006274 7.681000e-04 1.288000e-03 2.971900e-03 3.298270e-02 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.788801e-01 2.942742e-01 0.0000000 2.200002e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▇▇▆▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.788816e-01 2.942498e-01 0.0000000 2.186013e-01 4.715149e-01 7.338117e-01 9.999999e-01 ▇▇▆▆▆
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.035076e-01 2.568617e-01 0.0009930 1.316900e-02 7.791400e-02 3.164580e-01 9.990040e-01 ▇▂▁▁▁
numeric AF_reference 184849 0.9812372 NA NA NA NA NA NA NA 2.068392e-01 2.482924e-01 0.0000000 1.198080e-02 9.984030e-02 3.202880e-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 -0.0000724 0.0011545 0.9500000 0.9500179 0.623771 0.7821490 NA
1 54676 rs2462492 C T -0.0003404 0.0011437 0.7700005 0.7660106 0.400419 NA NA
1 86028 rs114608975 T C 0.0003000 0.0018287 0.8700001 0.8696902 0.103548 0.0277556 NA
1 91536 rs6702460 G T 0.0002539 0.0011261 0.8200001 0.8216387 0.456874 0.4207270 NA
1 234313 rs8179466 C T -0.0007221 0.0022207 0.7499995 0.7450443 0.074502 NA NA
1 534192 rs6680723 C T 0.0005561 0.0012865 0.6700003 0.6655486 0.240960 NA NA
1 546697 rs12025928 A G -0.0015584 0.0016045 0.3300000 0.3314361 0.913458 NA NA
1 693731 rs12238997 A G 0.0004332 0.0010779 0.6899999 0.6877891 0.116343 0.1417730 NA
1 705882 rs72631875 G A 0.0025520 0.0015796 0.1100001 0.1061797 0.067292 0.0315495 NA
1 706368 rs55727773 A G 0.0003875 0.0007985 0.6300007 0.6274428 0.515635 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219704 rs147475742 G A -0.0004571 0.0016761 0.7899998 0.7850524 0.041947 0.0473243 NA
22 51219766 rs182321900 C T -0.0065900 0.0078128 0.4000000 0.3989535 0.001935 NA NA
22 51220146 rs868950473 C T -0.0055080 0.0077380 0.4799997 0.4765801 0.001984 NA NA
22 51221190 rs369304721 G A 0.0011922 0.0016733 0.4799997 0.4761580 0.049720 NA NA
22 51221731 rs115055839 T C 0.0010377 0.0012516 0.4100001 0.4070493 0.073216 0.0625000 NA
22 51222100 rs114553188 G T 0.0014889 0.0014732 0.3100002 0.3121740 0.054467 0.0880591 NA
22 51223637 rs375798137 G A 0.0014364 0.0014803 0.3300000 0.3318683 0.054096 0.0788738 NA
22 51229805 rs9616985 T C 0.0010533 0.0012561 0.4000000 0.4017358 0.073051 0.0730831 NA
22 51232488 rs376461333 A G -0.0007783 0.0029586 0.7899998 0.7925087 0.020044 NA NA
22 51237063 rs3896457 T C 0.0003287 0.0007682 0.6700003 0.6687373 0.297983 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623771 ES:SE:LP:AF:ID  -7.23697e-05:0.00115451:0.0222764:0.623771:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400419 ES:SE:LP:AF:ID  -0.000340362:0.0011437:0.113509:0.400419:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103548 ES:SE:LP:AF:ID  0.000300002:0.0018287:0.0604807:0.103548:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456874 ES:SE:LP:AF:ID  0.00025387:0.00112612:0.0861861:0.456874:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074502 ES:SE:LP:AF:ID  -0.000722119:0.00222067:0.124939:0.074502:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24096  ES:SE:LP:AF:ID  0.000556092:0.00128646:0.173925:0.24096:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913458 ES:SE:LP:AF:ID  -0.00155836:0.00160453:0.481486:0.913458:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116343 ES:SE:LP:AF:ID  0.00043317:0.00107792:0.161151:0.116343:rs12238997
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1   714596  rs149887893 T   C   .   PASS    AF=0.033003 ES:SE:LP:AF:ID  -0.00148756:0.00201323:0.337242:0.033003:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.03662  ES:SE:LP:AF:ID  -0.00160674:0.00182863:0.420216:0.03662:rs12184267
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1   717485  rs12184279  C   A   .   PASS    AF=0.036436 ES:SE:LP:AF:ID  -0.00161546:0.00183483:0.420216:0.036436:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016407 ES:SE:LP:AF:ID  0.00311102:0.00282518:0.568636:0.016407:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036976 ES:SE:LP:AF:ID  -0.00134762:0.00181449:0.337242:0.036976:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037073 ES:SE:LP:AF:ID  -0.00150025:0.00180826:0.387216:0.037073:rs12565286
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1   723891  rs2977670   G   C   .   PASS    AF=0.959099 ES:SE:LP:AF:ID  0.0020989:0.00174412:0.638272:0.959099:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031449 ES:SE:LP:AF:ID  -0.000733633:0.00316633:0.0861861:0.031449:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053261 ES:SE:LP:AF:ID  -0.00267058:0.00251812:0.537602:0.053261:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036591 ES:SE:LP:AF:ID  -0.00180654:0.00181996:0.49485:0.036591:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036908 ES:SE:LP:AF:ID  -0.00170316:0.00180337:0.468521:0.036908:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843199 ES:SE:LP:AF:ID  -0.00023529:0.000934207:0.09691:0.843199:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055923 ES:SE:LP:AF:ID  -0.00127731:0.00151252:0.39794:0.055923:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122323 ES:SE:LP:AF:ID  0.000843521:0.00102253:0.387216:0.122323:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025706 ES:SE:LP:AF:ID  0.000512933:0.00251569:0.0757207:0.025706:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.121565 ES:SE:LP:AF:ID  0.000947458:0.00102296:0.455932:0.121565:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132348 ES:SE:LP:AF:ID  -4.36521e-05:0.00100824:0.0132283:0.132348:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.01113  ES:SE:LP:AF:ID  0.00625668:0.00366655:1.05552:0.01113:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.005701 ES:SE:LP:AF:ID  0.000916333:0.00473176:0.0705811:0.005701:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002267 ES:SE:LP:AF:ID  -0.0154856:0.00796124:1.284:0.002267:rs112573343
1   746189  rs139221807 A   G   .   PASS    AF=0.001029 ES:SE:LP:AF:ID  -0.0060235:0.013034:0.19382:0.001029:rs139221807
1   752478  rs146277091 G   A   .   PASS    AF=0.036822 ES:SE:LP:AF:ID  -0.00151277:0.00178516:0.39794:0.036822:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838941 ES:SE:LP:AF:ID  0.000177236:0.000904721:0.0757207:0.838941:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838569 ES:SE:LP:AF:ID  0.000109278:0.000903747:0.0457575:0.838569:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869769 ES:SE:LP:AF:ID  -0.00010754:0.000969756:0.0409586:0.869769:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129881 ES:SE:LP:AF:ID  0.000245605:0.000971734:0.09691:0.129881:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037333 ES:SE:LP:AF:ID  -0.00132632:0.00175491:0.346787:0.037333:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037576 ES:SE:LP:AF:ID  -0.00128904:0.00174383:0.337242:0.037576:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869112 ES:SE:LP:AF:ID  -0.000186337:0.000967857:0.0705811:0.869112:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86921  ES:SE:LP:AF:ID  -0.000171054:0.000968241:0.0655015:0.86921:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037534 ES:SE:LP:AF:ID  -0.00157567:0.00175138:0.431798:0.037534:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869114 ES:SE:LP:AF:ID  -0.000183811:0.000967837:0.0705811:0.869114:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005119 ES:SE:LP:AF:ID  -0.0109742:0.00497102:1.56864:0.005119:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.005085 ES:SE:LP:AF:ID  -0.0109738:0.00498405:1.55284:0.005085:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.838022 ES:SE:LP:AF:ID  0.000148031:0.000901234:0.0604807:0.838022:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037546 ES:SE:LP:AF:ID  -0.00158325:0.00175385:0.431798:0.037546:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838653 ES:SE:LP:AF:ID  0.000190474:0.000903769:0.0809219:0.838653:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013773 ES:SE:LP:AF:ID  0.000899688:0.00315453:0.107905:0.013773:rs181660517