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-18818/UKB-b-18818_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18818/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:19 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18818/UKB-b-18818_data.vcf.gz ...
Read summary statistics for 4236043 SNPs.
Dropped 870 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, 995618 SNPs remain.
After merging with regression SNP LD, 995618 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0007 (0.0011)
Lambda GC: 1.0306
Mean Chi^2: 1.0359
Intercept: 1.0283 (0.0089)
Ratio: 0.789 (0.2495)
Analysis finished at Thu Oct 17 14:41:09 2019
Total time elapsed: 50.04s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.8843,
    "inflation_factor": 1.0475,
    "mean_EFFECT": -7.5971e-07,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 1,
    "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": 34823,
    "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": 995618,
    "ldsc_nsnp_merge_regression_ld": 995618,
    "ldsc_observed_scale_h2_beta": 0.0007,
    "ldsc_observed_scale_h2_se": 0.0011,
    "ldsc_intercept_beta": 1.0283,
    "ldsc_intercept_se": 0.0089,
    "ldsc_lambda_gc": 1.0306,
    "ldsc_mean_chisq": 1.0359,
    "ldsc_ratio": 0.7883
}

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 1.0000000 3 58 0 4235178 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 4236043 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 8.655337e+00 5.765428e+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 7.859390e+07 5.673220e+07 828.0000000 3.167594e+07 6.897308e+07 1.146775e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -8.000000e-07 2.185000e-04 -0.0012502 -1.439000e-04 -9.000000e-07 1.423000e-04 1.177500e-03 ▁▁▇▂▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.112000e-04 3.270000e-05 0.0001706 1.829000e-04 2.002000e-04 2.333000e-04 6.238000e-04 ▇▁▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.932777e-01 2.906589e-01 0.0000000 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.932733e-01 2.906343e-01 0.0000000 2.398275e-01 4.908923e-01 7.452597e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.841542e-01 2.198593e-01 0.1040740 1.930070e-01 3.319330e-01 5.449460e-01 8.959260e-01 ▇▅▃▂▂
numeric AF_reference 34823 0.9917794 NA NA NA NA NA NA NA 3.743034e-01 2.217527e-01 0.0000000 1.916930e-01 3.300720e-01 5.331470e-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 -0.0000439 0.0003139 0.8900000 0.8887802 0.623767 0.782149 NA
1 54676 rs2462492 C T -0.0000742 0.0003110 0.8100000 0.8113174 0.400393 NA NA
1 91536 rs6702460 G T 0.0003074 0.0003062 0.3200000 0.3154761 0.456846 0.420727 NA
1 534192 rs6680723 C T 0.0004208 0.0003498 0.2300001 0.2289929 0.240954 NA NA
1 693731 rs12238997 A G -0.0005223 0.0002932 0.0749998 0.0747961 0.116316 0.141773 NA
1 706368 rs55727773 A G -0.0000417 0.0002172 0.8499999 0.8477130 0.515642 0.275160 NA
1 729679 rs4951859 C G 0.0005588 0.0002540 0.0280001 0.0278405 0.843210 0.639976 NA
1 731718 rs142557973 T C -0.0004412 0.0002781 0.1100001 0.1126122 0.122303 0.154353 NA
1 734349 rs141242758 T C -0.0004428 0.0002782 0.1100001 0.1114214 0.121544 0.152556 NA
1 736289 rs79010578 T A -0.0006483 0.0002742 0.0179999 0.0180605 0.132331 0.139577 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51198027 rs34939255 A G -0.0000564 0.0002165 0.7899998 0.7946113 0.254557 0.0984425 NA
22 51208537 rs72619593 G A 0.0002641 0.0002894 0.3599996 0.3615697 0.120757 0.1142170 NA
22 51210289 rs112565862 C T 0.0000038 0.0002883 0.9900000 0.9895045 0.129974 0.1018370 NA
22 51211106 rs9628250 T C 0.0000490 0.0002147 0.8200001 0.8195354 0.271547 0.1671330 NA
22 51211392 rs3888396 T C 0.0000554 0.0002857 0.8499999 0.8462024 0.132655 0.1641370 NA
22 51212875 rs2238837 A C 0.0001736 0.0002040 0.3900004 0.3947431 0.331453 0.3724040 NA
22 51213613 rs34726907 C T -0.0002251 0.0002688 0.4000000 0.4023817 0.127791 0.1727240 NA
22 51216564 rs9616970 T C -0.0002304 0.0002676 0.3900004 0.3892494 0.128306 0.1563500 NA
22 51219006 rs28729663 G A -0.0002420 0.0002619 0.3599996 0.3554500 0.137928 0.2052720 NA
22 51237063 rs3896457 T C 0.0001266 0.0002088 0.5400003 0.5442689 0.297974 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623767 ES:SE:LP:AF:ID  -4.39041e-05:0.000313942:0.05061:0.623767:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400393 ES:SE:LP:AF:ID  -7.42469e-05:0.000311012:0.091515:0.400393:rs2462492
1   91536   rs6702460   G   T   .   PASS    AF=0.456846 ES:SE:LP:AF:ID  0.000307401:0.000306238:0.49485:0.456846:rs6702460
1   534192  rs6680723   C   T   .   PASS    AF=0.240954 ES:SE:LP:AF:ID  0.000420804:0.000349808:0.638272:0.240954:rs6680723
1   693731  rs12238997  A   G   .   PASS    AF=0.116316 ES:SE:LP:AF:ID  -0.000522309:0.00029315:1.12494:0.116316:rs12238997
1   706368  rs12029736  A   G   .   PASS    AF=0.515642 ES:SE:LP:AF:ID  -4.17011e-05:0.000217151:0.0705811:0.515642:rs12029736
1   729679  rs4951859   C   G   .   PASS    AF=0.84321  ES:SE:LP:AF:ID  0.000558781:0.000254046:1.55284:0.84321:rs4951859
1   731718  rs58276399  T   C   .   PASS    AF=0.122303 ES:SE:LP:AF:ID  -0.000441186:0.000278077:0.958607:0.122303:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.121544 ES:SE:LP:AF:ID  -0.000442841:0.000278195:0.958607:0.121544:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132331 ES:SE:LP:AF:ID  -0.000648276:0.000274185:1.74473:0.132331:rs79010578
1   752566  rs3094315   G   A   .   PASS    AF=0.838945 ES:SE:LP:AF:ID  0.000550459:0.000246023:1.60206:0.838945:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838573 ES:SE:LP:AF:ID  0.000553364:0.000245758:1.61979:0.838573:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869783 ES:SE:LP:AF:ID  0.000439635:0.000263712:1.02228:0.869783:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129869 ES:SE:LP:AF:ID  -0.000439418:0.000264249:1.01773:0.129869:rs2073813
1   754182  rs3131969   A   G   .   PASS    AF=0.869124 ES:SE:LP:AF:ID  0.00045559:0.000263195:1.08092:0.869124:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869222 ES:SE:LP:AF:ID  0.000477177:0.000263299:1.1549:0.869222:rs3131968
1   754334  rs3131967   T   C   .   PASS    AF=0.869128 ES:SE:LP:AF:ID  0.000454049:0.00026319:1.07572:0.869128:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.838025 ES:SE:LP:AF:ID  0.000558175:0.000245075:1.63827:0.838025:rs3115859
1   754964  rs3131966   C   T   .   PASS    AF=0.838655 ES:SE:LP:AF:ID  0.00055098:0.000245761:1.60206:0.838655:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839768 ES:SE:LP:AF:ID  0.00051803:0.000249087:1.42022:0.839768:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.869407 ES:SE:LP:AF:ID  0.000438324:0.000262885:1.02228:0.869407:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868953 ES:SE:LP:AF:ID  0.000435527:0.000262223:1.01323:0.868953:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867905 ES:SE:LP:AF:ID  0.000457272:0.000261721:1.09151:0.867905:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869097 ES:SE:LP:AF:ID  0.000440489:0.000262438:1.03152:0.869097:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.869105 ES:SE:LP:AF:ID  0.000440433:0.000262458:1.03152:0.869105:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.869113 ES:SE:LP:AF:ID  0.000444552:0.000262464:1.04576:0.869113:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.869591 ES:SE:LP:AF:ID  0.000437638:0.000263185:1.01773:0.869591:rs3131954
1   760912  rs1048488   C   T   .   PASS    AF=0.838306 ES:SE:LP:AF:ID  0.000540155:0.000244611:1.56864:0.838306:rs1048488
1   761147  rs3115850   T   C   .   PASS    AF=0.838427 ES:SE:LP:AF:ID  0.000539041:0.000244784:1.55284:0.838427:rs3115850
1   761732  rs2286139   C   T   .   PASS    AF=0.862262 ES:SE:LP:AF:ID  0.000483586:0.000261516:1.19382:0.862262:rs2286139
1   763394  rs3115847   G   A   .   PASS    AF=0.706758 ES:SE:LP:AF:ID  0.000321403:0.000254605:0.677781:0.706758:rs3115847
1   766007  rs61768174  A   C   .   PASS    AF=0.105136 ES:SE:LP:AF:ID  -0.000394052:0.000293286:0.744727:0.105136:rs61768174
1   768253  rs2977608   A   C   .   PASS    AF=0.761296 ES:SE:LP:AF:ID  8.43447e-05:0.000207784:0.167491:0.761296:rs2977608
1   768448  rs12562034  G   A   .   PASS    AF=0.106499 ES:SE:LP:AF:ID  0.000387474:0.000286381:0.744727:0.106499:rs12562034
1   769223  rs60320384  C   G   .   PASS    AF=0.129576 ES:SE:LP:AF:ID  -0.000412155:0.00026409:0.920819:0.129576:rs60320384
1   771823  rs2977605   T   C   .   PASS    AF=0.868914 ES:SE:LP:AF:ID  0.000451435:0.000262683:1.0655:0.868914:rs2977605
1   771967  rs59066358  G   A   .   PASS    AF=0.129676 ES:SE:LP:AF:ID  -0.00042458:0.00026392:0.958607:0.129676:rs59066358
1   772755  rs2905039   A   C   .   PASS    AF=0.868924 ES:SE:LP:AF:ID  0.000446459:0.000262688:1.05061:0.868924:rs2905039
1   776546  rs12124819  A   G   .   PASS    AF=0.265384 ES:SE:LP:AF:ID  0.000401764:0.000232105:1.08092:0.265384:rs12124819
1   777122  rs2980319   A   T   .   PASS    AF=0.870047 ES:SE:LP:AF:ID  0.000481098:0.000263224:1.16749:0.870047:rs2980319
1   778745  rs1055606   A   G   .   PASS    AF=0.128574 ES:SE:LP:AF:ID  -0.00044728:0.00026426:1.04096:0.128574:rs1055606
1   779322  rs4040617   A   G   .   PASS    AF=0.128869 ES:SE:LP:AF:ID  -0.000451509:0.000263813:1.06048:0.128869:rs4040617
1   780785  rs2977612   T   A   .   PASS    AF=0.868787 ES:SE:LP:AF:ID  0.000490049:0.00026252:1.20761:0.868787:rs2977612
1   782981  rs6594026   C   T   .   PASS    AF=0.12951  ES:SE:LP:AF:ID  -0.000460946:0.000263727:1.09691:0.12951:rs6594026
1   785050  rs2905062   G   A   .   PASS    AF=0.86854  ES:SE:LP:AF:ID  0.000497759:0.00026246:1.23657:0.86854:rs2905062
1   785989  rs2980300   T   C   .   PASS    AF=0.868481 ES:SE:LP:AF:ID  0.00049866:0.000262623:1.23657:0.868481:rs2980300
1   787399  rs2905055   G   T   .   PASS    AF=0.860783 ES:SE:LP:AF:ID  0.000495104:0.000262444:1.22915:0.860783:rs2905055
1   787606  rs3863622   G   T   .   PASS    AF=0.128458 ES:SE:LP:AF:ID  -0.000455205:0.000265149:1.0655:0.128458:rs3863622
1   787685  rs2905054   G   T   .   PASS    AF=0.861494 ES:SE:LP:AF:ID  0.000496657:0.000262614:1.22915:0.861494:rs2905054
1   787844  rs2905053   C   T   .   PASS    AF=0.869231 ES:SE:LP:AF:ID  0.00049816:0.000263584:1.22915:0.869231:rs2905053