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-7425/UKB-b-7425_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7425/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-7425/UKB-b-7425_data.vcf.gz ...
Read summary statistics for 4111168 SNPs.
Dropped 800 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, 973142 SNPs remain.
After merging with regression SNP LD, 973142 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0023 (0.001)
Lambda GC: 1.0421
Mean Chi^2: 1.0348
Intercept: 1.0121 (0.0075)
Ratio: 0.3463 (0.2165)
Analysis finished at Thu Oct 17 14:41:40 2019
Total time elapsed: 1.0m:22.0s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.8797,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -4.5939e-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": 33631,
    "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": 973142,
    "ldsc_nsnp_merge_regression_ld": 973142,
    "ldsc_observed_scale_h2_beta": 0.0023,
    "ldsc_observed_scale_h2_se": 0.001,
    "ldsc_intercept_beta": 1.0121,
    "ldsc_intercept_se": 0.0075,
    "ldsc_lambda_gc": 1.0421,
    "ldsc_mean_chisq": 1.0348,
    "ldsc_ratio": 0.3477
}

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 4110373 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 4111168 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 8.656532e+00 5.766713e+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 7.858113e+07 5.674784e+07 828.0000000 3.164519e+07 6.892125e+07 1.146904e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -5.000000e-07 2.078000e-04 -0.0012409 -1.378000e-04 -1.000000e-06 1.360000e-04 1.121000e-03 ▁▁▇▂▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.016000e-04 2.930000e-05 0.0001647 1.762000e-04 1.919000e-04 2.217000e-04 6.023000e-04 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.935510e-01 2.900000e-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 4.935504e-01 2.899719e-01 0.0000004 2.417466e-01 4.904824e-01 7.445819e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.895882e-01 2.162643e-01 0.1113590 2.013180e-01 3.398810e-01 5.488510e-01 8.886410e-01 ▇▅▃▂▂
numeric AF_reference 33631 0.9918196 NA NA NA NA NA NA NA 3.791009e-01 2.192820e-01 0.0000000 1.986820e-01 3.368610e-01 5.369410e-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 -0.0000308 0.0003031 0.9199999 0.9189327 0.623765 0.782149 NA
1 54676 rs2462492 C T 0.0002252 0.0003002 0.4500005 0.4533013 0.400401 NA NA
1 91536 rs6702460 G T 0.0001657 0.0002956 0.5800000 0.5751853 0.456846 0.420727 NA
1 534192 rs6680723 C T -0.0003164 0.0003377 0.3500000 0.3488036 0.240959 NA NA
1 693731 rs12238997 A G -0.0000212 0.0002830 0.9400001 0.9403364 0.116329 0.141773 NA
1 706368 rs55727773 A G 0.0001969 0.0002096 0.3500000 0.3476502 0.515645 0.275160 NA
1 729679 rs4951859 C G 0.0000085 0.0002452 0.9699999 0.9721897 0.843204 0.639976 NA
1 731718 rs142557973 T C -0.0000466 0.0002684 0.8600001 0.8622190 0.122312 0.154353 NA
1 734349 rs141242758 T C -0.0000624 0.0002685 0.8200001 0.8163738 0.121554 0.152556 NA
1 736289 rs79010578 T A -0.0002553 0.0002647 0.3300000 0.3347685 0.132335 0.139577 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51198027 rs34939255 A G -0.0000879 0.0002090 0.6700003 0.6741406 0.254562 0.0984425 NA
22 51208537 rs72619593 G A -0.0001343 0.0002794 0.6300007 0.6308534 0.120735 0.1142170 NA
22 51210289 rs112565862 C T 0.0005294 0.0002783 0.0569994 0.0571570 0.129958 0.1018370 NA
22 51211106 rs9628250 T C -0.0000116 0.0002073 0.9599999 0.9552011 0.271547 0.1671330 NA
22 51211392 rs3888396 T C 0.0004642 0.0002758 0.0920005 0.0923508 0.132638 0.1641370 NA
22 51212875 rs2238837 A C 0.0001241 0.0001969 0.5300002 0.5284556 0.331457 0.3724040 NA
22 51213613 rs34726907 C T -0.0006452 0.0002595 0.0129999 0.0128871 0.127814 0.1727240 NA
22 51216564 rs9616970 T C -0.0006728 0.0002583 0.0092000 0.0092066 0.128328 0.1563500 NA
22 51219006 rs28729663 G A -0.0006777 0.0002529 0.0074000 0.0073616 0.137950 0.2052720 NA
22 51237063 rs3896457 T C 0.0003016 0.0002016 0.1299999 0.1346318 0.297974 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623765 ES:SE:LP:AF:ID  -3.08445e-05:0.000303056:0.0362122:0.623765:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  0.000225153:0.000300235:0.346787:0.400401:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.456846 ES:SE:LP:AF:ID  0.000165673:0.000295617:0.236572:0.456846:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.240959 ES:SE:LP:AF:ID  -0.000316371:0.000337674:0.455932:0.240959:rs6680723
1   693731  rs12238997  A   G   .   PASS    AF=0.116329 ES:SE:LP:AF:ID  -2.11803e-05:0.000282981:0.0268721:0.116329:rs12238997
1   706368  rs12029736  A   G   .   PASS    AF=0.515645 ES:SE:LP:AF:ID  0.000196867:0.000209621:0.455932:0.515645:rs12029736
1   729679  rs4951859   C   G   .   PASS    AF=0.843204 ES:SE:LP:AF:ID  8.54958e-06:0.00024524:0.0132283:0.843204:rs4951859
1   731718  rs58276399  T   C   .   PASS    AF=0.122312 ES:SE:LP:AF:ID  -4.65871e-05:0.000268436:0.0655015:0.122312:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121554 ES:SE:LP:AF:ID  -6.23599e-05:0.000268548:0.0861861:0.121554:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132335 ES:SE:LP:AF:ID  -0.000255299:0.000264681:0.481486:0.132335:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.838945 ES:SE:LP:AF:ID  0.000136257:0.000237498:0.244125:0.838945:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838573 ES:SE:LP:AF:ID  0.000133334:0.000237243:0.244125:0.838573:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869776 ES:SE:LP:AF:ID  0.000169895:0.00025457:0.30103:0.869776:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129876 ES:SE:LP:AF:ID  -0.00016619:0.000255091:0.29243:0.129876:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.869117 ES:SE:LP:AF:ID  0.000185976:0.000254071:0.337242:0.869117:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869215 ES:SE:LP:AF:ID  0.000180832:0.000254171:0.318759:0.869215:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869121 ES:SE:LP:AF:ID  0.000175092:0.000254066:0.309804:0.869121:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838026 ES:SE:LP:AF:ID  0.000152933:0.000236584:0.283997:0.838026:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.838657 ES:SE:LP:AF:ID  0.00016425:0.000237249:0.309804:0.838657:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.83977  ES:SE:LP:AF:ID  0.000172623:0.000240458:0.327902:0.83977:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.8694   ES:SE:LP:AF:ID  0.000190863:0.000253772:0.346787:0.8694:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868947 ES:SE:LP:AF:ID  0.000179305:0.000253133:0.318759:0.868947:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867898 ES:SE:LP:AF:ID  0.000182389:0.000252648:0.327902:0.867898:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.86909  ES:SE:LP:AF:ID  0.000185357:0.000253341:0.337242:0.86909:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869098 ES:SE:LP:AF:ID  0.000185443:0.00025336:0.337242:0.869098:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869106 ES:SE:LP:AF:ID  0.000189276:0.000253366:0.337242:0.869106:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869584 ES:SE:LP:AF:ID  0.000185416:0.000254062:0.327902:0.869584:rs3131954
1   760912  rs1048488   C   T   .   PASS    AF=0.838306 ES:SE:LP:AF:ID  0.000147181:0.000236135:0.275724:0.838306:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.838427 ES:SE:LP:AF:ID  0.000143998:0.000236302:0.267606:0.838427:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.862254 ES:SE:LP:AF:ID  0.000169157:0.00025245:0.30103:0.862254:rs2286139
1   763394  rs3115847   G   A   .   PASS    AF=0.706753 ES:SE:LP:AF:ID  0.000142127:0.000245761:0.251812:0.706753:rs3115847
1   768253  rs2977608   A   C   .   PASS    AF=0.761297 ES:SE:LP:AF:ID  0.000122912:0.000200584:0.267606:0.761297:rs2977608
1   769223  rs60320384  C   G   .   PASS    AF=0.129581 ES:SE:LP:AF:ID  -0.000179679:0.000254938:0.318759:0.129581:rs60320384
1   771823  rs2977605   T   C   .   PASS    AF=0.868906 ES:SE:LP:AF:ID  0.000164252:0.000253576:0.283997:0.868906:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.129682 ES:SE:LP:AF:ID  -0.000159136:0.000254774:0.275724:0.129682:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.868916 ES:SE:LP:AF:ID  0.000162254:0.000253581:0.283997:0.868916:rs2905039
1   776546  rs12124819  A   G   .   PASS    AF=0.265385 ES:SE:LP:AF:ID  0.000320791:0.000224059:0.823909:0.265385:rs12124819
1   777122  rs2980319   A   T   .   PASS    AF=0.870039 ES:SE:LP:AF:ID  0.000134481:0.000254099:0.221849:0.870039:rs2980319
1   778745  rs1055606   A   G   .   PASS    AF=0.12858  ES:SE:LP:AF:ID  -0.000124623:0.000255102:0.200659:0.12858:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.128877 ES:SE:LP:AF:ID  -0.000107393:0.000254669:0.173925:0.128877:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.86878  ES:SE:LP:AF:ID  0.000123964:0.000253421:0.207608:0.86878:rs2977612
1   782981  rs6594026   C   T   .   PASS    AF=0.129518 ES:SE:LP:AF:ID  -0.000130304:0.000254587:0.21467:0.129518:rs6594026
1   785050  rs2905062   G   A   .   PASS    AF=0.868533 ES:SE:LP:AF:ID  0.000143918:0.000253362:0.244125:0.868533:rs2905062
1   785989  rs2980300   T   C   .   PASS    AF=0.868474 ES:SE:LP:AF:ID  0.000145223:0.000253519:0.244125:0.868474:rs2980300
1   787399  rs2905055   G   T   .   PASS    AF=0.860776 ES:SE:LP:AF:ID  0.000104564:0.000253346:0.167491:0.860776:rs2905055
1   787606  rs3863622   G   T   .   PASS    AF=0.128466 ES:SE:LP:AF:ID  -0.000135711:0.00025596:0.221849:0.128466:rs3863622
1   787685  rs2905054   G   T   .   PASS    AF=0.861487 ES:SE:LP:AF:ID  0.000116244:0.000253512:0.187087:0.861487:rs2905054
1   787844  rs2905053   C   T   .   PASS    AF=0.869224 ES:SE:LP:AF:ID  0.000148478:0.000254447:0.251812:0.869224:rs2905053
1   791191  rs111818025 G   A   .   PASS    AF=0.127884 ES:SE:LP:AF:ID  -0.000151609:0.0002576:0.251812:0.127884:rs111818025
1   795988  rs59380221  C   T   .   PASS    AF=0.143053 ES:SE:LP:AF:ID  -0.000565763:0.000262775:1.50864:0.143053:rs59380221