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_2754.vcf.gz --id UKB-b:12405 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2754.txt.gz --cohort_controls 170498 --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-12405/UKB-b-12405_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12405/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:54 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12405/UKB-b-12405_data.vcf.gz ...
Read summary statistics for 9395175 SNPs.
Dropped 10909 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, 1288148 SNPs remain.
After merging with regression SNP LD, 1288148 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.1485 (0.0063)
Lambda GC: 1.4573
Mean Chi^2: 1.5662
Intercept: 1.0665 (0.0089)
Ratio: 0.1174 (0.0157)
Analysis finished at Thu Oct 17 14:43:38 2019
Total time elapsed: 1.0m:44.43s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9488,
    "inflation_factor": 1.3107,
    "mean_EFFECT": -0.0002,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 35,
    "n_p_sig": 1323,
    "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": 121605,
    "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": 1288148,
    "ldsc_nsnp_merge_regression_ld": 1288148,
    "ldsc_observed_scale_h2_beta": 0.1485,
    "ldsc_observed_scale_h2_se": 0.0063,
    "ldsc_intercept_beta": 1.0665,
    "ldsc_intercept_se": 0.0089,
    "ldsc_lambda_gc": 1.4573,
    "ldsc_mean_chisq": 1.5662,
    "ldsc_ratio": 0.1174
}

Flags

name value
af_correlation FALSE
inflation_factor TRUE
n TRUE
is_snpid_non_unique FALSE
mean_EFFECT_nonfinite FALSE
mean_EFFECT_05 FALSE
mean_EFFECT_01 FALSE
mean_chisq TRUE
n_p_sig TRUE
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 9384322 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 9395175 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 8.631351e+00 5.752758e+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 7.882563e+07 5.630584e+07 828.0000000 3.252276e+07 6.941147e+07 1.145540e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.952000e-04 1.358800e-02 -0.1804090 -5.360100e-03 -1.005000e-04 5.138300e-03 1.895940e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 9.875700e-03 8.012000e-03 0.0032167 3.890600e-03 6.202200e-03 1.349760e-02 1.151740e-01 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.547191e-01 3.008416e-01 0.0000000 1.800002e-01 4.400003e-01 7.199992e-01 1.000000e+00 ▇▆▆▅▆
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.547221e-01 3.008171e-01 0.0000000 1.829867e-01 4.384278e-01 7.151575e-01 9.999998e-01 ▇▆▆▅▅
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.119046e-01 2.575731e-01 0.0020530 1.666900e-02 8.983100e-02 3.321980e-01 9.979470e-01 ▇▂▁▁▁
numeric AF_reference 121605 0.9870567 NA NA NA NA NA NA NA 2.131969e-01 2.493387e-01 0.0000000 1.417730e-02 1.088260e-01 3.326680e-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 -0.0067757 0.0059255 0.2500000 0.2528374 0.623593 0.7821490 NA
1 54676 rs2462492 C T 0.0014668 0.0058536 0.8000000 0.8021412 0.401333 NA NA
1 86028 rs114608975 T C 0.0033488 0.0093996 0.7199992 0.7216393 0.103583 0.0277556 NA
1 91536 rs6702460 G T -0.0024174 0.0057737 0.6800001 0.6754382 0.457054 0.4207270 NA
1 234313 rs8179466 C T 0.0060310 0.0114632 0.5999997 0.5988084 0.074264 NA NA
1 534192 rs6680723 C T -0.0049816 0.0066024 0.4500005 0.4505466 0.241068 NA NA
1 546697 rs12025928 A G -0.0078059 0.0082531 0.3400001 0.3442403 0.913747 NA NA
1 693731 rs12238997 A G -0.0059346 0.0055209 0.2800000 0.2824039 0.116883 0.1417730 NA
1 705882 rs72631875 G A 0.0154195 0.0081698 0.0589997 0.0591083 0.066489 0.0315495 NA
1 706368 rs55727773 A G 0.0071636 0.0041018 0.0810009 0.0807337 0.514839 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0014169 0.0049619 0.7800007 0.7752211 0.138219 0.2052720 NA
22 51219387 rs9616832 T C -0.0000512 0.0064428 0.9900000 0.9936574 0.073651 0.0654952 NA
22 51219704 rs147475742 G A 0.0024985 0.0086371 0.7700005 0.7723750 0.041869 0.0473243 NA
22 51221190 rs369304721 G A -0.0007616 0.0086265 0.9299999 0.9296473 0.049538 NA NA
22 51221731 rs115055839 T C 0.0000106 0.0064443 1.0000000 0.9986914 0.073191 0.0625000 NA
22 51222100 rs114553188 G T -0.0048369 0.0075494 0.5199996 0.5217157 0.054928 0.0880591 NA
22 51223637 rs375798137 G A -0.0044950 0.0075867 0.5500004 0.5535292 0.054545 0.0788738 NA
22 51229805 rs9616985 T C 0.0001092 0.0064666 0.9900000 0.9865232 0.073070 0.0730831 NA
22 51232488 rs376461333 A G -0.0018976 0.0150418 0.9000000 0.8996097 0.020361 NA NA
22 51237063 rs3896457 T C -0.0087255 0.0039471 0.0269998 0.0270624 0.298280 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623593 ES:SE:LP:AF:ID  -0.00677573:0.00592551:0.60206:0.623593:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.401333 ES:SE:LP:AF:ID  0.00146678:0.00585361:0.09691:0.401333:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103583 ES:SE:LP:AF:ID  0.00334877:0.00939956:0.142668:0.103583:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.457054 ES:SE:LP:AF:ID  -0.00241743:0.00577371:0.167491:0.457054:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074264 ES:SE:LP:AF:ID  0.00603096:0.0114632:0.221849:0.074264:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.241068 ES:SE:LP:AF:ID  -0.00498156:0.00660243:0.346787:0.241068:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913747 ES:SE:LP:AF:ID  -0.00780594:0.00825309:0.468521:0.913747:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116883 ES:SE:LP:AF:ID  -0.00593464:0.00552093:0.552842:0.116883:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.066489 ES:SE:LP:AF:ID  0.0154195:0.00816976:1.22915:0.066489:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514839 ES:SE:LP:AF:ID  0.00716355:0.00410179:1.09151:0.514839:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032982 ES:SE:LP:AF:ID  0.00334275:0.0103319:0.124939:0.032982:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036569 ES:SE:LP:AF:ID  0.00439481:0.00939404:0.19382:0.036569:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036651 ES:SE:LP:AF:ID  0.00395426:0.00936369:0.173925:0.036651:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.036338 ES:SE:LP:AF:ID  0.00361074:0.00943388:0.154902:0.036338:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016287 ES:SE:LP:AF:ID  -0.00709804:0.0145355:0.200659:0.016287:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.036872 ES:SE:LP:AF:ID  0.00310366:0.00932954:0.130768:0.036872:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036965 ES:SE:LP:AF:ID  0.00394918:0.00929828:0.173925:0.036965:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101112 ES:SE:LP:AF:ID  -0.000606938:0.00676609:0.0315171:0.101112:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959311 ES:SE:LP:AF:ID  -0.00399412:0.00897997:0.180456:0.959311:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03156  ES:SE:LP:AF:ID  0.00732758:0.0161036:0.187087:0.03156:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053493 ES:SE:LP:AF:ID  -0.00299722:0.0128704:0.0861861:0.053493:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036507 ES:SE:LP:AF:ID  0.00164386:0.00935308:0.0655015:0.036507:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036845 ES:SE:LP:AF:ID  0.00302714:0.00926744:0.130768:0.036845:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.842889 ES:SE:LP:AF:ID  0.00527324:0.00479504:0.568636:0.842889:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056064 ES:SE:LP:AF:ID  -0.0126931:0.00775132:1:0.056064:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.12285  ES:SE:LP:AF:ID  -0.00775911:0.00523847:0.853872:0.12285:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025822 ES:SE:LP:AF:ID  0.00829297:0.0128698:0.283997:0.025822:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122049 ES:SE:LP:AF:ID  -0.00766676:0.00524185:0.853872:0.122049:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132948 ES:SE:LP:AF:ID  -0.00447685:0.00516399:0.408935:0.132948:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011186 ES:SE:LP:AF:ID  0.000819128:0.0187593:0.0132283:0.011186:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.00586  ES:SE:LP:AF:ID  -0.0179255:0.0238088:0.346787:0.00586:rs61770167
1   742813  rs112573343 C   T   .   PASS    AF=0.002159 ES:SE:LP:AF:ID  -0.0109273:0.0419175:0.102373:0.002159:rs112573343
1   752478  rs146277091 G   A   .   PASS    AF=0.036766 ES:SE:LP:AF:ID  0.00441594:0.00917352:0.200659:0.036766:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838675 ES:SE:LP:AF:ID  0.00298271:0.00464379:0.283997:0.838675:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838307 ES:SE:LP:AF:ID  0.0033126:0.00463913:0.318759:0.838307:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869472 ES:SE:LP:AF:ID  0.00468703:0.00497504:0.455932:0.869472:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.13019  ES:SE:LP:AF:ID  -0.00450701:0.00498528:0.431798:0.13019:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037271 ES:SE:LP:AF:ID  0.0041218:0.00901824:0.187087:0.037271:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.03754  ES:SE:LP:AF:ID  0.00364552:0.00895639:0.167491:0.03754:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868825 ES:SE:LP:AF:ID  0.00488024:0.00496649:0.481486:0.868825:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868895 ES:SE:LP:AF:ID  0.00514398:0.00496828:0.522879:0.868895:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.03748  ES:SE:LP:AF:ID  0.00362676:0.00899824:0.161151:0.03748:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.86882  ES:SE:LP:AF:ID  0.00496008:0.00496637:0.49485:0.86882:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.004916 ES:SE:LP:AF:ID  0.000729087:0.0260797:0.00877392:0.004916:rs150578204
1   754458  rs142682604 G   T   .   PASS    AF=0.004885 ES:SE:LP:AF:ID  -1.41875e-05:0.0261471:-0:0.004885:rs142682604
1   754503  rs3115859   G   A   .   PASS    AF=0.837745 ES:SE:LP:AF:ID  0.00259861:0.00462561:0.244125:0.837745:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037498 ES:SE:LP:AF:ID  0.00468431:0.00901144:0.221849:0.037498:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.83837  ES:SE:LP:AF:ID  0.00276645:0.00463838:0.259637:0.83837:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013808 ES:SE:LP:AF:ID  -0.0206783:0.0161591:0.69897:0.013808:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005631 ES:SE:LP:AF:ID  -0.003653:0.0248127:0.0555173:0.005631:rs184270342