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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}

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-8088/UKB-b-8088_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8088/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:09 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-8088/UKB-b-8088_data.vcf.gz ...
Read summary statistics for 3798577 SNPs.
Dropped 665 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, 914533 SNPs remain.
After merging with regression SNP LD, 914533 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0004 (0.0013)
Lambda GC: 1.1363
Mean Chi^2: 1.1429
Intercept: 1.1387 (0.0089)
Ratio: 0.9712 (0.0621)
Analysis finished at Thu Oct 17 14:41:55 2019
Total time elapsed: 46.08s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.8666,
    "inflation_factor": 1.1474,
    "mean_EFFECT": 7.2071e-07,
    "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": 30795,
    "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": 914533,
    "ldsc_nsnp_merge_regression_ld": 914533,
    "ldsc_observed_scale_h2_beta": 0.0004,
    "ldsc_observed_scale_h2_se": 0.0013,
    "ldsc_intercept_beta": 1.1387,
    "ldsc_intercept_se": 0.0089,
    "ldsc_lambda_gc": 1.1363,
    "ldsc_mean_chisq": 1.1429,
    "ldsc_ratio": 0.9706
}

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 TRUE
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 1.000000 3 58 0 3797915 0 NA NA NA NA NA NA NA NA NA NA
character REF 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
character ALT 0 1.000000 1 1 0 4 0 NA NA NA NA NA NA NA NA NA NA
logical N 3798577 0.000000 NA NA NA NA NA NaN : NA NA NA NA NA NA NA NA
numeric CHROM 0 1.000000 NA NA NA NA NA NA NA 8.662450e+00 5.769547e+00 1.0000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▃▂▂
numeric POS 0 1.000000 NA NA NA NA NA NA NA 7.855885e+07 5.676171e+07 828.0000000 3.160391e+07 6.888965e+07 1.147124e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA 7.000000e-07 1.953000e-04 -0.0011211 -1.290000e-04 1.000000e-07 1.300000e-04 1.115300e-03 ▁▁▇▂▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 1.812000e-04 2.230000e-05 0.0001518 1.618000e-04 1.740000e-04 1.967000e-04 5.552000e-04 ▇▁▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 4.783080e-01 2.939371e-01 0.0000002 2.200002e-01 4.700002e-01 7.300002e-01 1.000000e+00 ▇▇▆▆▆
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 4.783074e-01 2.939113e-01 0.0000002 2.182646e-01 4.702059e-01 7.333982e-01 9.999998e-01 ▇▇▆▆▆
numeric AF 0 1.000000 NA NA NA NA NA NA NA 4.026043e-01 2.061669e-01 0.1307440 2.226190e-01 3.583800e-01 5.570250e-01 8.692550e-01 ▇▅▃▃▂
numeric AF_reference 30795 0.991893 NA NA NA NA NA NA NA 3.905967e-01 2.122694e-01 0.0000000 2.162540e-01 3.536340e-01 5.447280e-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 0.0004900 0.0002793 0.0790005 0.0793670 0.623765 0.782149 NA
1 54676 rs2462492 C T 0.0002414 0.0002767 0.3800004 0.3829392 0.400401 NA NA
1 91536 rs6702460 G T 0.0001117 0.0002724 0.6800001 0.6819150 0.456846 0.420727 NA
1 534192 rs6680723 C T -0.0002440 0.0003112 0.4299995 0.4330586 0.240959 NA NA
1 706368 rs55727773 A G -0.0001718 0.0001932 0.3700002 0.3738814 0.515645 0.275160 NA
1 729679 rs4951859 C G -0.0001240 0.0002260 0.5800000 0.5832639 0.843204 0.639976 NA
1 736289 rs79010578 T A 0.0000212 0.0002439 0.9299999 0.9306310 0.132335 0.139577 NA
1 752566 rs3094315 G A -0.0002945 0.0002189 0.1800002 0.1784121 0.838945 0.718251 NA
1 752721 rs3131972 A G -0.0002791 0.0002186 0.2000000 0.2017720 0.838573 0.653355 NA
1 754182 rs3131969 A G -0.0002486 0.0002341 0.2900000 0.2882934 0.869117 0.678514 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51186228 rs3865766 C T 0.0001185 0.0001649 0.4700002 0.4725069 0.451061 0.4532750 NA
22 51192586 rs5771006 G A 0.0003626 0.0002222 0.1000000 0.1026609 0.167627 0.0848642 NA
22 51193227 rs34608236 T G 0.0001381 0.0002271 0.5400003 0.5430573 0.168490 0.0692891 NA
22 51197266 rs61290853 A G 0.0001805 0.0001702 0.2900000 0.2891296 0.386333 0.4229230 NA
22 51198027 rs34939255 A G 0.0000486 0.0001927 0.8000000 0.8008433 0.254562 0.0984425 NA
22 51211106 rs9628250 T C -0.0001433 0.0001911 0.4500005 0.4532973 0.271547 0.1671330 NA
22 51211392 rs3888396 T C 0.0002048 0.0002543 0.4199997 0.4206029 0.132638 0.1641370 NA
22 51212875 rs2238837 A C 0.0001513 0.0001816 0.4000000 0.4045812 0.331457 0.3724040 NA
22 51219006 rs28729663 G A 0.0000065 0.0002331 0.9800000 0.9777076 0.137950 0.2052720 NA
22 51237063 rs3896457 T C 0.0002153 0.0001859 0.2500000 0.2466872 0.297974 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623765 ES:SE:LP:AF:ID  0.000489966:0.000279283:1.10237:0.623765:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  0.000241405:0.000276684:0.420216:0.400401:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.456846 ES:SE:LP:AF:ID  0.000111655:0.000272428:0.167491:0.456846:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.240959 ES:SE:LP:AF:ID  -0.00024396:0.000311186:0.366532:0.240959:rs6680723
1   706368  rs12029736  A   G   .   PASS    AF=0.515645 ES:SE:LP:AF:ID  -0.000171779:0.000193178:0.431798:0.515645:rs12029736
1   729679  rs4951859   C   G   .   PASS    AF=0.843204 ES:SE:LP:AF:ID  -0.00012399:0.000226002:0.236572:0.843204:rs4951859
1   736289  rs79010578  T   A   .   PASS    AF=0.132335 ES:SE:LP:AF:ID  2.12334e-05:0.000243919:0.0315171:0.132335:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.838945 ES:SE:LP:AF:ID  -0.000294522:0.000218868:0.744727:0.838945:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838573 ES:SE:LP:AF:ID  -0.000279088:0.000218632:0.69897:0.838573:rs3131972
1   754182  rs3131969   A   G   .   PASS    AF=0.869117 ES:SE:LP:AF:ID  -0.000248628:0.000234141:0.537602:0.869117:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869215 ES:SE:LP:AF:ID  -0.000254168:0.000234233:0.552842:0.869215:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869121 ES:SE:LP:AF:ID  -0.000248532:0.000234136:0.537602:0.869121:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838026 ES:SE:LP:AF:ID  -0.0002735:0.000218026:0.677781:0.838026:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.838657 ES:SE:LP:AF:ID  -0.000269216:0.000218638:0.657577:0.838657:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.83977  ES:SE:LP:AF:ID  -0.000255461:0.000221595:0.60206:0.83977:rs3131965
1   756604  rs3131962   A   G   .   PASS    AF=0.868947 ES:SE:LP:AF:ID  -0.000287705:0.000233277:0.657577:0.868947:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867898 ES:SE:LP:AF:ID  -0.000263234:0.00023283:0.585027:0.867898:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.86909  ES:SE:LP:AF:ID  -0.000276301:0.000233468:0.619789:0.86909:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869098 ES:SE:LP:AF:ID  -0.000276125:0.000233486:0.619789:0.869098:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869106 ES:SE:LP:AF:ID  -0.000276378:0.000233491:0.619789:0.869106:rs3131956
1   760912  rs1048488   C   T   .   PASS    AF=0.838306 ES:SE:LP:AF:ID  -0.000269989:0.000217612:0.677781:0.838306:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.838427 ES:SE:LP:AF:ID  -0.000271496:0.000217766:0.677781:0.838427:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.862254 ES:SE:LP:AF:ID  -0.000230826:0.000232647:0.49485:0.862254:rs2286139
1   763394  rs3115847   G   A   .   PASS    AF=0.706753 ES:SE:LP:AF:ID  -0.000127311:0.000226483:0.244125:0.706753:rs3115847
1   768253  rs2977608   A   C   .   PASS    AF=0.761297 ES:SE:LP:AF:ID  -0.000157905:0.000184849:0.408935:0.761297:rs2977608
1   771823  rs2977605   T   C   .   PASS    AF=0.868906 ES:SE:LP:AF:ID  -0.000273471:0.000233685:0.619789:0.868906:rs2977605
1   772755  rs2905039   A   C   .   PASS    AF=0.868916 ES:SE:LP:AF:ID  -0.000275042:0.00023369:0.619789:0.868916:rs2905039
1   776546  rs12124819  A   G   .   PASS    AF=0.265385 ES:SE:LP:AF:ID  -0.000206154:0.000206484:0.49485:0.265385:rs12124819
1   780785  rs2977612   T   A   .   PASS    AF=0.86878  ES:SE:LP:AF:ID  -0.00024789:0.000233542:0.537602:0.86878:rs2977612
1   785050  rs2905062   G   A   .   PASS    AF=0.868533 ES:SE:LP:AF:ID  -0.000266987:0.000233487:0.60206:0.868533:rs2905062
1   785989  rs2980300   T   C   .   PASS    AF=0.868474 ES:SE:LP:AF:ID  -0.000265858:0.000233633:0.585027:0.868474:rs2980300
1   787399  rs2905055   G   T   .   PASS    AF=0.860776 ES:SE:LP:AF:ID  -0.000269782:0.000233473:0.60206:0.860776:rs2905055
1   787685  rs2905054   G   T   .   PASS    AF=0.861487 ES:SE:LP:AF:ID  -0.000311177:0.000233625:0.744727:0.861487:rs2905054
1   787844  rs2905053   C   T   .   PASS    AF=0.869224 ES:SE:LP:AF:ID  -0.000278678:0.000234487:0.638272:0.869224:rs2905053
1   795988  rs59380221  C   T   .   PASS    AF=0.143053 ES:SE:LP:AF:ID  0.000125783:0.000242163:0.221849:0.143053:rs59380221
1   798400  rs10900604  A   G   .   PASS    AF=0.206591 ES:SE:LP:AF:ID  6.95748e-05:0.000197196:0.142668:0.206591:rs10900604
1   798959  rs11240777  G   A   .   PASS    AF=0.20642  ES:SE:LP:AF:ID  6.03788e-05:0.000197281:0.119186:0.20642:rs11240777
1   808631  rs11240779  G   A   .   PASS    AF=0.772619 ES:SE:LP:AF:ID  7.34372e-05:0.000187669:0.154902:0.772619:rs11240779
1   808928  rs11240780  C   T   .   PASS    AF=0.772847 ES:SE:LP:AF:ID  5.55108e-05:0.000187985:0.113509:0.772847:rs11240780
1   814495  rs74461805  C   A   .   PASS    AF=0.340396 ES:SE:LP:AF:ID  -0.000102251:0.00026488:0.154902:0.340396:rs74461805
1   824398  rs7538305   A   C   .   PASS    AF=0.138699 ES:SE:LP:AF:ID  5.37862e-06:0.000233917:0.00877392:0.138699:rs7538305
1   830181  rs28444699  A   G   .   PASS    AF=0.697255 ES:SE:LP:AF:ID  -0.000325297:0.000177219:1.18046:0.697255:rs28444699
1   831489  rs4970385   C   T   .   PASS    AF=0.705397 ES:SE:LP:AF:ID  -0.000367907:0.000174011:1.46852:0.705397:rs4970385
1   831909  rs9697642   C   T   .   PASS    AF=0.705442 ES:SE:LP:AF:ID  -0.000369299:0.000174005:1.46852:0.705442:rs9697642
1   832066  rs9697380   G   C   .   PASS    AF=0.705627 ES:SE:LP:AF:ID  -0.000372749:0.000174014:1.49485:0.705627:rs9697380
1   832318  rs4500250   C   A   .   PASS    AF=0.705655 ES:SE:LP:AF:ID  -0.000373505:0.000174032:1.49485:0.705655:rs4500250
1   832398  rs4553118   T   C   .   PASS    AF=0.730129 ES:SE:LP:AF:ID  -0.000353798:0.000178774:1.31876:0.730129:rs4553118
1   832918  rs28765502  T   C   .   PASS    AF=0.294377 ES:SE:LP:AF:ID  0.000372729:0.000174024:1.49485:0.294377:rs28765502
1   833223  rs13303211  C   T   .   PASS    AF=0.236703 ES:SE:LP:AF:ID  0.000593087:0.000185275:2.85387:0.236703:rs13303211
1   833302  rs28752186  C   T   .   PASS    AF=0.236691 ES:SE:LP:AF:ID  0.000599283:0.000185277:2.92082:0.236691:rs28752186