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-13920/UKB-b-13920_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13920/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:05 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-13920/UKB-b-13920_data.vcf.gz ...
Read summary statistics for 4268416 SNPs.
Dropped 886 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, 1001074 SNPs remain.
After merging with regression SNP LD, 1001074 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0014 (0.0011)
Lambda GC: 1.0268
Mean Chi^2: 1.0243
Intercept: 1.0104 (0.0086)
Ratio: 0.4263 (0.356)
Analysis finished at Thu Oct 17 14:43:57 2019
Total time elapsed: 52.32s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.8855,
    "inflation_factor": 1,
    "mean_EFFECT": -3.7278e-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": 35152,
    "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": 1001074,
    "ldsc_nsnp_merge_regression_ld": 1001074,
    "ldsc_observed_scale_h2_beta": 0.0014,
    "ldsc_observed_scale_h2_se": 0.0011,
    "ldsc_intercept_beta": 1.0104,
    "ldsc_intercept_se": 0.0086,
    "ldsc_lambda_gc": 1.0268,
    "ldsc_mean_chisq": 1.0243,
    "ldsc_ratio": 0.428
}

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 4267535 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 4268416 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 8.655107e+00 5.765776e+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 7.858935e+07 5.672462e+07 828.0000000 3.169410e+07 6.895694e+07 1.146797e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -4.000000e-07 2.177000e-04 -0.0014244 -1.437000e-04 -1.000000e-06 1.421000e-04 1.507200e-03 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.130000e-04 3.350000e-05 0.0001715 1.840000e-04 2.017000e-04 2.356000e-04 6.273000e-04 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.959799e-01 2.892775e-01 0.0000003 2.399999e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.959800e-01 2.892509e-01 0.0000003 2.447698e-01 4.950428e-01 7.461705e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.828024e-01 2.207913e-01 0.1022200 1.909020e-01 3.300210e-01 5.440390e-01 8.977800e-01 ▇▅▃▂▂
numeric AF_reference 35152 0.9917646 NA NA NA NA NA NA NA 3.730818e-01 2.224221e-01 0.0000000 1.898960e-01 3.282750e-01 5.321490e-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 0.0003527 0.0003157 0.2599998 0.2638903 0.623765 0.7821490 NA
1 54676 rs2462492 C T -0.0007918 0.0003127 0.0109999 0.0113376 0.400401 NA NA
1 86028 rs114608975 T C 0.0005013 0.0005000 0.3200000 0.3160355 0.103556 0.0277556 NA
1 91536 rs6702460 G T 0.0001792 0.0003079 0.5600000 0.5606641 0.456846 0.4207270 NA
1 534192 rs6680723 C T -0.0005899 0.0003517 0.0929994 0.0934754 0.240959 NA NA
1 693731 rs12238997 A G -0.0002529 0.0002947 0.3900004 0.3908412 0.116329 0.1417730 NA
1 706368 rs55727773 A G -0.0000739 0.0002183 0.7300002 0.7349661 0.515645 0.2751600 NA
1 729679 rs4951859 C G 0.0001804 0.0002554 0.4799997 0.4799666 0.843204 0.6399760 NA
1 731718 rs142557973 T C -0.0002478 0.0002796 0.3800004 0.3755241 0.122312 0.1543530 NA
1 734349 rs141242758 T C -0.0002485 0.0002797 0.3700002 0.3743784 0.121554 0.1525560 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51198027 rs34939255 A G 0.0000043 0.0002177 0.9800000 0.9841836 0.254562 0.0984425 NA
22 51208537 rs72619593 G A 0.0002019 0.0002910 0.4899999 0.4878185 0.120735 0.1142170 NA
22 51210289 rs112565862 C T -0.0002005 0.0002899 0.4899999 0.4892386 0.129958 0.1018370 NA
22 51211106 rs9628250 T C 0.0001444 0.0002159 0.5000000 0.5035163 0.271547 0.1671330 NA
22 51211392 rs3888396 T C -0.0001337 0.0002873 0.6400000 0.6417023 0.132638 0.1641370 NA
22 51212875 rs2238837 A C -0.0002556 0.0002051 0.2099999 0.2127568 0.331457 0.3724040 NA
22 51213613 rs34726907 C T 0.0001549 0.0002702 0.5700002 0.5665751 0.127814 0.1727240 NA
22 51216564 rs9616970 T C 0.0001931 0.0002691 0.4700002 0.4730157 0.128328 0.1563500 NA
22 51219006 rs28729663 G A 0.0002633 0.0002634 0.3200000 0.3174300 0.137950 0.2052720 NA
22 51237063 rs3896457 T C -0.0001559 0.0002100 0.4600002 0.4577617 0.297974 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623765 ES:SE:LP:AF:ID  0.000352659:0.000315651:0.585027:0.623765:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400401 ES:SE:LP:AF:ID  -0.000791826:0.000312713:1.95861:0.400401:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103556 ES:SE:LP:AF:ID  0.000501282:0.000499963:0.49485:0.103556:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456846 ES:SE:LP:AF:ID  0.000179155:0.000307903:0.251812:0.456846:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.240959 ES:SE:LP:AF:ID  -0.000589935:0.000351708:1.03152:0.240959:rs6680723
1   693731  rs12238997  A   G   .   PASS    AF=0.116329 ES:SE:LP:AF:ID  -0.000252916:0.000294742:0.408935:0.116329:rs12238997
1   706368  rs12029736  A   G   .   PASS    AF=0.515645 ES:SE:LP:AF:ID  -7.39116e-05:0.000218333:0.136677:0.515645:rs12029736
1   729679  rs4951859   C   G   .   PASS    AF=0.843204 ES:SE:LP:AF:ID  0.000180426:0.000255432:0.318759:0.843204:rs4951859
1   731718  rs58276399  T   C   .   PASS    AF=0.122312 ES:SE:LP:AF:ID  -0.000247767:0.000279592:0.420216:0.122312:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121554 ES:SE:LP:AF:ID  -0.000248466:0.000279709:0.431798:0.121554:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132335 ES:SE:LP:AF:ID  -0.000223436:0.000275682:0.376751:0.132335:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.838945 ES:SE:LP:AF:ID  0.000184377:0.000247368:0.337242:0.838945:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838573 ES:SE:LP:AF:ID  0.000219224:0.000247102:0.431798:0.838573:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869776 ES:SE:LP:AF:ID  0.0002287:0.00026515:0.408935:0.869776:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129876 ES:SE:LP:AF:ID  -0.00028815:0.000265692:0.552842:0.129876:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.869117 ES:SE:LP:AF:ID  0.000255748:0.00026463:0.481486:0.869117:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869215 ES:SE:LP:AF:ID  0.000259056:0.000264735:0.481486:0.869215:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869121 ES:SE:LP:AF:ID  0.000254409:0.000264625:0.468521:0.869121:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838026 ES:SE:LP:AF:ID  0.000205507:0.000246416:0.39794:0.838026:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.838657 ES:SE:LP:AF:ID  0.000199094:0.000247109:0.376751:0.838657:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.83977  ES:SE:LP:AF:ID  0.000188446:0.000250451:0.346787:0.83977:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.8694   ES:SE:LP:AF:ID  0.000217992:0.000264319:0.387216:0.8694:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868947 ES:SE:LP:AF:ID  0.000234068:0.000263654:0.431798:0.868947:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867898 ES:SE:LP:AF:ID  0.000236719:0.000263148:0.431798:0.867898:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.86909  ES:SE:LP:AF:ID  0.000224942:0.00026387:0.408935:0.86909:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869098 ES:SE:LP:AF:ID  0.000228383:0.00026389:0.408935:0.869098:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869106 ES:SE:LP:AF:ID  0.000228003:0.000263896:0.408935:0.869106:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869584 ES:SE:LP:AF:ID  0.000216645:0.000264621:0.387216:0.869584:rs3131954
1   760912  rs1048488   C   T   .   PASS    AF=0.838306 ES:SE:LP:AF:ID  0.000195182:0.000245949:0.366532:0.838306:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.838427 ES:SE:LP:AF:ID  0.000190696:0.000246123:0.356547:0.838427:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.862254 ES:SE:LP:AF:ID  0.000251654:0.000262942:0.468521:0.862254:rs2286139
1   763394  rs3115847   G   A   .   PASS    AF=0.706753 ES:SE:LP:AF:ID  0.000157606:0.000255975:0.267606:0.706753:rs3115847
1   766007  rs61768174  A   C   .   PASS    AF=0.105145 ES:SE:LP:AF:ID  -0.000353965:0.000294878:0.638272:0.105145:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.761297 ES:SE:LP:AF:ID  0.000203232:0.00020892:0.481486:0.761297:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.10649  ES:SE:LP:AF:ID  -0.000109481:0.000287957:0.154902:0.10649:rs12562034
1   769223  rs60320384  C   G   .   PASS    AF=0.129581 ES:SE:LP:AF:ID  -0.000303455:0.000265534:0.60206:0.129581:rs60320384
1   771823  rs2977605   T   C   .   PASS    AF=0.868906 ES:SE:LP:AF:ID  0.000271218:0.000264115:0.522879:0.868906:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.129682 ES:SE:LP:AF:ID  -0.000301693:0.000265362:0.585027:0.129682:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.868916 ES:SE:LP:AF:ID  0.000273381:0.00026412:0.522879:0.868916:rs2905039
1   776546  rs12124819  A   G   .   PASS    AF=0.265385 ES:SE:LP:AF:ID  0.000344714:0.000233371:0.853872:0.265385:rs12124819
1   777122  rs2980319   A   T   .   PASS    AF=0.870039 ES:SE:LP:AF:ID  0.000290445:0.000264659:0.568636:0.870039:rs2980319
1   778745  rs1055606   A   G   .   PASS    AF=0.12858  ES:SE:LP:AF:ID  -0.000316244:0.000265704:0.638272:0.12858:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.128877 ES:SE:LP:AF:ID  -0.000303461:0.000265253:0.60206:0.128877:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.86878  ES:SE:LP:AF:ID  0.000283516:0.000263953:0.552842:0.86878:rs2977612
1   782981  rs6594026   C   T   .   PASS    AF=0.129518 ES:SE:LP:AF:ID  -0.000305129:0.000265168:0.60206:0.129518:rs6594026
1   785050  rs2905062   G   A   .   PASS    AF=0.868533 ES:SE:LP:AF:ID  0.000267796:0.000263891:0.508638:0.868533:rs2905062
1   785989  rs2980300   T   C   .   PASS    AF=0.868474 ES:SE:LP:AF:ID  0.000265433:0.000264056:0.508638:0.868474:rs2980300
1   787399  rs2905055   G   T   .   PASS    AF=0.860776 ES:SE:LP:AF:ID  0.000261571:0.000263876:0.49485:0.860776:rs2905055
1   787606  rs3863622   G   T   .   PASS    AF=0.128466 ES:SE:LP:AF:ID  -0.000298646:0.000266597:0.585027:0.128466:rs3863622
1   787685  rs2905054   G   T   .   PASS    AF=0.861487 ES:SE:LP:AF:ID  0.000168097:0.000264048:0.283997:0.861487:rs2905054