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_22502.vcf.gz --id UKB-b:16858 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_22502.txt.gz --cohort_cases 15367 --cohort_controls 97216 --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-16858/UKB-b-16858_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16858/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:45:18 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-16858/UKB-b-16858_data.vcf.gz ...
Read summary statistics for 6560277 SNPs.
Dropped 3622 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, 1246379 SNPs remain.
After merging with regression SNP LD, 1246379 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.042 (0.0046)
Lambda GC: 1.1033
Mean Chi^2: 1.0949
Intercept: 0.9982 (0.0065)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:46:31 2019
Total time elapsed: 1.0m:12.6s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9314,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 3,
    "n_p_sig": 24,
    "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": 60206,
    "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": 1246379,
    "ldsc_nsnp_merge_regression_ld": 1246379,
    "ldsc_observed_scale_h2_beta": 0.042,
    "ldsc_observed_scale_h2_se": 0.0046,
    "ldsc_intercept_beta": 0.9982,
    "ldsc_intercept_se": 0.0065,
    "ldsc_lambda_gc": 1.1033,
    "ldsc_mean_chisq": 1.0949,
    "ldsc_ratio": -0.019
}

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 6556677 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 6560277 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 8.665826e+00 5.764008e+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 7.858170e+07 5.648518e+07 828.0000000 3.204093e+07 6.902002e+07 1.144851e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.430000e-05 2.592200e-03 -0.0222170 -1.497500e-03 -1.600000e-05 1.462800e-03 2.155190e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.329400e-03 9.483000e-04 0.0013954 1.559900e-03 1.943500e-03 2.866500e-03 1.188920e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.856341e-01 2.920125e-01 0.0000000 2.300001e-01 4.799997e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.856346e-01 2.919876e-01 0.0000000 2.294208e-01 4.803202e-01 7.381687e-01 9.999994e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.909625e-01 2.577840e-01 0.0227770 7.437700e-02 2.007450e-01 4.520720e-01 9.772230e-01 ▇▃▂▂▁
numeric AF_reference 60206 0.9908226 NA NA NA NA NA NA NA 2.885876e-01 2.500994e-01 0.0000000 8.366610e-02 2.106630e-01 4.440890e-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 -0.0009217 0.0025652 0.7199992 0.7193609 0.624290 0.7821490 NA
1 54676 rs2462492 C T 0.0005680 0.0025428 0.8200001 0.8232560 0.400055 NA NA
1 86028 rs114608975 T C 0.0080625 0.0040686 0.0479999 0.0475201 0.103422 0.0277556 NA
1 91536 rs6702460 G T -0.0041793 0.0025045 0.0949992 0.0951712 0.456878 0.4207270 NA
1 234313 rs8179466 C T 0.0037733 0.0049428 0.4500005 0.4452252 0.074494 NA NA
1 534192 rs6680723 C T 0.0001069 0.0028627 0.9699999 0.9702015 0.240639 NA NA
1 546697 rs12025928 A G -0.0009736 0.0035604 0.7800007 0.7845133 0.913377 NA NA
1 693731 rs12238997 A G 0.0000433 0.0023975 0.9900000 0.9855923 0.116942 0.1417730 NA
1 705882 rs72631875 G A 0.0010611 0.0034962 0.7600007 0.7615175 0.067453 0.0315495 NA
1 706368 rs55727773 A G 0.0011765 0.0017754 0.5099998 0.5075483 0.515771 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C -0.0022304 0.0027891 0.4199997 0.4238891 0.072944 0.0826677 NA
22 51219006 rs28729663 G A 0.0001652 0.0021514 0.9400001 0.9387784 0.136400 0.2052720 NA
22 51219387 rs9616832 T C -0.0025529 0.0027936 0.3599996 0.3608039 0.073116 0.0654952 NA
22 51219704 rs147475742 G A -0.0012771 0.0037572 0.7300002 0.7339363 0.041354 0.0473243 NA
22 51221190 rs369304721 G A -0.0021861 0.0037426 0.5600000 0.5591499 0.049198 NA NA
22 51221731 rs115055839 T C -0.0021196 0.0027968 0.4500005 0.4485289 0.072556 0.0625000 NA
22 51222100 rs114553188 G T 0.0022093 0.0033000 0.5000000 0.5031901 0.053686 0.0880591 NA
22 51223637 rs375798137 G A 0.0023123 0.0033164 0.4899999 0.4856605 0.053335 0.0788738 NA
22 51229805 rs9616985 T C -0.0020059 0.0028072 0.4700002 0.4748895 0.072363 0.0730831 NA
22 51237063 rs3896457 T C 0.0000238 0.0017051 0.9900000 0.9888421 0.297871 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.62429  ES:SE:LP:AF:ID  -0.000921728:0.00256525:0.142668:0.62429:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.400055 ES:SE:LP:AF:ID  0.000567951:0.00254277:0.0861861:0.400055:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103422 ES:SE:LP:AF:ID  0.00806246:0.00406859:1.31876:0.103422:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456878 ES:SE:LP:AF:ID  -0.00417928:0.00250447:1.02228:0.456878:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074494 ES:SE:LP:AF:ID  0.0037733:0.00494276:0.346787:0.074494:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.240639 ES:SE:LP:AF:ID  0.000106939:0.00286273:0.0132283:0.240639:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913377 ES:SE:LP:AF:ID  -0.000973572:0.00356043:0.107905:0.913377:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.116942 ES:SE:LP:AF:ID  4.32948e-05:0.00239749:0.00436481:0.116942:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067453 ES:SE:LP:AF:ID  0.00106106:0.00349621:0.119186:0.067453:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.515771 ES:SE:LP:AF:ID  0.0011765:0.00177542:0.29243:0.515771:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032576 ES:SE:LP:AF:ID  0.00354813:0.00450992:0.366532:0.032576:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.036179 ES:SE:LP:AF:ID  0.00315531:0.00409526:0.356547:0.036179:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036289 ES:SE:LP:AF:ID  0.002653:0.00407925:0.283997:0.036289:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.035992 ES:SE:LP:AF:ID  0.00297476:0.00410898:0.327902:0.035992:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.036544 ES:SE:LP:AF:ID  0.00298107:0.00406161:0.337242:0.036544:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.03665  ES:SE:LP:AF:ID  0.00316899:0.00404663:0.366532:0.03665:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.101881 ES:SE:LP:AF:ID  0.000686024:0.00291919:0.091515:0.101881:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959784 ES:SE:LP:AF:ID  -0.00454291:0.00391373:0.60206:0.959784:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031358 ES:SE:LP:AF:ID  0.0129059:0.007081:1.16749:0.031358:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.053215 ES:SE:LP:AF:ID  0.000951748:0.00559143:0.0655015:0.053215:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03616  ES:SE:LP:AF:ID  0.0035435:0.00407252:0.420216:0.03616:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03643  ES:SE:LP:AF:ID  0.00396724:0.00403817:0.481486:0.03643:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843316 ES:SE:LP:AF:ID  -0.00113406:0.00208305:0.229148:0.843316:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056231 ES:SE:LP:AF:ID  -0.0015306:0.00335433:0.187087:0.056231:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122818 ES:SE:LP:AF:ID  -0.00013185:0.0022755:0.0222764:0.122818:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025023 ES:SE:LP:AF:ID  -0.000561527:0.00566586:0.0362122:0.025023:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122073 ES:SE:LP:AF:ID  -7.01485e-05:0.00227698:0.00877392:0.122073:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132198 ES:SE:LP:AF:ID  0.00196021:0.00224613:0.420216:0.132198:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.03633  ES:SE:LP:AF:ID  0.00374189:0.00399681:0.455932:0.03633:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.838812 ES:SE:LP:AF:ID  -0.000372841:0.00201701:0.0705811:0.838812:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838513 ES:SE:LP:AF:ID  -0.000475997:0.00201494:0.091515:0.838513:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869129 ES:SE:LP:AF:ID  0.000599961:0.00215818:0.107905:0.869129:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130516 ES:SE:LP:AF:ID  -0.000246699:0.00216255:0.0409586:0.130516:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.036859 ES:SE:LP:AF:ID  0.00375389:0.00392673:0.468521:0.036859:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037121 ES:SE:LP:AF:ID  0.00384971:0.00389964:0.49485:0.037121:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868532 ES:SE:LP:AF:ID  0.000515128:0.0021541:0.091515:0.868532:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868621 ES:SE:LP:AF:ID  0.000498164:0.00215498:0.0861861:0.868621:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037058 ES:SE:LP:AF:ID  0.00369204:0.0039195:0.455932:0.037058:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868535 ES:SE:LP:AF:ID  0.000546279:0.00215411:0.09691:0.868535:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.83787  ES:SE:LP:AF:ID  -0.000407338:0.00200894:0.0757207:0.83787:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.037085 ES:SE:LP:AF:ID  0.00357853:0.00392425:0.443698:0.037085:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838477 ES:SE:LP:AF:ID  -0.000358062:0.00201432:0.0655015:0.838477:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839547 ES:SE:LP:AF:ID  -0.000213506:0.00204092:0.0362122:0.839547:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868704 ES:SE:LP:AF:ID  0.000637798:0.00215116:0.113509:0.868704:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868221 ES:SE:LP:AF:ID  0.000660482:0.00214552:0.119186:0.868221:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867275 ES:SE:LP:AF:ID  0.000548814:0.00214154:0.09691:0.867275:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868358 ES:SE:LP:AF:ID  0.000602324:0.00214731:0.107905:0.868358:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.868366 ES:SE:LP:AF:ID  0.000599407:0.00214746:0.107905:0.868366:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.868368 ES:SE:LP:AF:ID  0.000597731:0.00214746:0.107905:0.868368:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.868878 ES:SE:LP:AF:ID  0.000631366:0.00215348:0.113509:0.868878:rs3131954