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_20147.vcf.gz --id UKB-b:378 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_20147.txt.gz --cohort_controls 25044 --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-378/UKB-b-378_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-378/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:47 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-378/UKB-b-378_data.vcf.gz ...
Read summary statistics for 7251389 SNPs.
Dropped 4714 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, 1270601 SNPs remain.
After merging with regression SNP LD, 1270601 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0054 (0.0176)
Lambda GC: 0.9988
Mean Chi^2: 1.005
Intercept: 1.0024 (0.0059)
Ratio: 0.4746 (1.1786)
Analysis finished at Thu Oct 17 14:45:10 2019
Total time elapsed: 1.0m:22.53s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9378,
    "inflation_factor": 1,
    "mean_EFFECT": 0,
    "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": 66974,
    "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": 1270601,
    "ldsc_nsnp_merge_regression_ld": 1270601,
    "ldsc_observed_scale_h2_beta": 0.0054,
    "ldsc_observed_scale_h2_se": 0.0176,
    "ldsc_intercept_beta": 1.0024,
    "ldsc_intercept_se": 0.0059,
    "ldsc_lambda_gc": 0.9988,
    "ldsc_mean_chisq": 1.005,
    "ldsc_ratio": 0.48
}

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 1.000000 3 58 0 7246697 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 7251389 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 8.663534e+00 5.763857e+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 7.863073e+07 5.645591e+07 828.0000000 3.215680e+07 6.904869e+07 1.145047e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.000000 NA NA NA NA NA NA NA 3.510000e-05 1.631770e-02 -0.1573180 -8.356800e-03 4.500000e-05 8.406400e-03 1.721150e-01 ▁▁▇▁▁
numeric SE 0 1.000000 NA NA NA NA NA NA NA 1.460290e-02 7.308300e-03 0.0077070 8.771100e-03 1.149940e-02 1.856020e-02 8.135920e-02 ▇▂▁▁▁
numeric PVAL 0 1.000000 NA NA NA NA NA NA NA 5.001082e-01 2.892792e-01 0.0000004 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.000000 NA NA NA NA NA NA NA 5.001090e-01 2.892552e-01 0.0000004 2.489396e-01 5.007292e-01 7.508166e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.000000 NA NA NA NA NA NA NA 2.677473e-01 2.604431e-01 0.0139760 5.263800e-02 1.680520e-01 4.213790e-01 9.860240e-01 ▇▂▂▁▁
numeric AF_reference 66974 0.990764 NA NA NA NA NA NA NA 2.663261e-01 2.523354e-01 0.0000000 5.910540e-02 1.809110e-01 4.147360e-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 -0.0042918 0.0142119 0.7600007 0.7626651 0.624189 0.7821490 NA
1 54676 rs2462492 C T -0.0127315 0.0140584 0.3700002 0.3651396 0.397788 NA NA
1 86028 rs114608975 T C -0.0303221 0.0226942 0.1800002 0.1815111 0.102593 0.0277556 NA
1 91536 rs6702460 G T 0.0053723 0.0137629 0.6999999 0.6962809 0.456056 0.4207270 NA
1 234313 rs8179466 C T -0.0069986 0.0273603 0.8000000 0.7981096 0.074390 NA NA
1 534192 rs6680723 C T 0.0095807 0.0158390 0.5500004 0.5452577 0.241930 NA NA
1 546697 rs12025928 A G 0.0068248 0.0196907 0.7300002 0.7288932 0.912569 NA NA
1 693731 rs12238997 A G 0.0080076 0.0132320 0.5500004 0.5450670 0.117146 0.1417730 NA
1 705882 rs72631875 G A -0.0098019 0.0191979 0.6100002 0.6096523 0.068497 0.0315495 NA
1 706368 rs55727773 A G -0.0104947 0.0098359 0.2900000 0.2859827 0.514749 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0167098 0.0117832 0.1600000 0.1561605 0.139459 0.2052720 NA
22 51219387 rs9616832 T C 0.0078561 0.0152172 0.6100002 0.6056697 0.074797 0.0654952 NA
22 51219704 rs147475742 G A -0.0125701 0.0203891 0.5400003 0.5375575 0.042919 0.0473243 NA
22 51221190 rs369304721 G A 0.0032158 0.0204563 0.8800001 0.8750842 0.050340 NA NA
22 51221731 rs115055839 T C 0.0071378 0.0152166 0.6400000 0.6390107 0.074345 0.0625000 NA
22 51222100 rs114553188 G T 0.0205858 0.0180177 0.2500000 0.2532329 0.055541 0.0880591 NA
22 51223637 rs375798137 G A 0.0194095 0.0181100 0.2800000 0.2838297 0.055201 0.0788738 NA
22 51229805 rs9616985 T C 0.0079942 0.0152767 0.5999997 0.6007676 0.074153 0.0730831 NA
22 51232488 rs376461333 A G 0.0309341 0.0367782 0.4000000 0.4002926 0.020141 NA NA
22 51237063 rs3896457 T C -0.0146972 0.0094500 0.1199999 0.1198863 0.296882 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.624189 ES:SE:LP:AF:ID  -0.00429175:0.0142119:0.119186:0.624189:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.397788 ES:SE:LP:AF:ID  -0.0127315:0.0140584:0.431798:0.397788:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.102593 ES:SE:LP:AF:ID  -0.0303221:0.0226942:0.744727:0.102593:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.456056 ES:SE:LP:AF:ID  0.00537229:0.0137629:0.154902:0.456056:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.07439  ES:SE:LP:AF:ID  -0.00699861:0.0273603:0.09691:0.07439:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.24193  ES:SE:LP:AF:ID  0.00958072:0.015839:0.259637:0.24193:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912569 ES:SE:LP:AF:ID  0.00682476:0.0196907:0.136677:0.912569:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117146 ES:SE:LP:AF:ID  0.00800759:0.013232:0.259637:0.117146:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.068497 ES:SE:LP:AF:ID  -0.00980187:0.0191979:0.21467:0.068497:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514749 ES:SE:LP:AF:ID  -0.0104947:0.00983593:0.537602:0.514749:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.032126 ES:SE:LP:AF:ID  -0.00349161:0.0251017:0.05061:0.032126:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.035579 ES:SE:LP:AF:ID  -0.00696583:0.0228323:0.119186:0.035579:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.035717 ES:SE:LP:AF:ID  -0.00704796:0.0227419:0.119186:0.035717:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.035383 ES:SE:LP:AF:ID  -0.0039723:0.0229172:0.0655015:0.035383:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016558 ES:SE:LP:AF:ID  -0.00544309:0.0346593:0.0555173:0.016558:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.035848 ES:SE:LP:AF:ID  -0.00733212:0.0226787:0.124939:0.035848:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.035963 ES:SE:LP:AF:ID  -0.00594241:0.0225991:0.102373:0.035963:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.100899 ES:SE:LP:AF:ID  -0.00565917:0.0162726:0.136677:0.100899:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.96017  ES:SE:LP:AF:ID  0.00243116:0.0217226:0.0409586:0.96017:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031889 ES:SE:LP:AF:ID  -0.0137305:0.0383823:0.142668:0.031889:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052258 ES:SE:LP:AF:ID  0.00212729:0.0318508:0.0222764:0.052258:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.035536 ES:SE:LP:AF:ID  -0.00394396:0.0227119:0.0655015:0.035536:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.035821 ES:SE:LP:AF:ID  -0.00487984:0.0224974:0.0809219:0.035821:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.843413 ES:SE:LP:AF:ID  -0.00962076:0.0115242:0.39794:0.843413:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055736 ES:SE:LP:AF:ID  0.0233739:0.0186927:0.677781:0.055736:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.122882 ES:SE:LP:AF:ID  0.0112957:0.012586:0.431798:0.122882:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.024982 ES:SE:LP:AF:ID  -0.0366187:0.0312861:0.619789:0.024982:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122131 ES:SE:LP:AF:ID  0.0116942:0.012588:0.455932:0.122131:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132112 ES:SE:LP:AF:ID  0.00597023:0.0124601:0.200659:0.132112:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.035843 ES:SE:LP:AF:ID  -0.00268311:0.0222438:0.0457575:0.035843:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839191 ES:SE:LP:AF:ID  -0.0108366:0.0111612:0.481486:0.839191:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.838744 ES:SE:LP:AF:ID  -0.0113581:0.0111447:0.508638:0.838744:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.869296 ES:SE:LP:AF:ID  -0.0124914:0.0119432:0.522879:0.869296:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130466 ES:SE:LP:AF:ID  0.0132415:0.0119608:0.568636:0.130466:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.036419 ES:SE:LP:AF:ID  0.000449325:0.021837:0.00877392:0.036419:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.036655 ES:SE:LP:AF:ID  -0.000743632:0.0217056:0.0132283:0.036655:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.868518 ES:SE:LP:AF:ID  -0.0131705:0.0119182:0.568636:0.868518:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868629 ES:SE:LP:AF:ID  -0.0130295:0.0119263:0.568636:0.868629:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.036588 ES:SE:LP:AF:ID  0.000687681:0.0217908:0.0132283:0.036588:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.868509 ES:SE:LP:AF:ID  -0.0131741:0.0119174:0.568636:0.868509:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.83812  ES:SE:LP:AF:ID  -0.0111217:0.0111109:0.49485:0.83812:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.036607 ES:SE:LP:AF:ID  0.000136248:0.0218162:-0:0.036607:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.838733 ES:SE:LP:AF:ID  -0.0111887:0.0111409:0.49485:0.838733:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.839858 ES:SE:LP:AF:ID  -0.011068:0.0112837:0.481486:0.839858:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868785 ES:SE:LP:AF:ID  -0.0115133:0.0119011:0.481486:0.868785:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.868112 ES:SE:LP:AF:ID  -0.011446:0.0118597:0.481486:0.868112:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.867034 ES:SE:LP:AF:ID  -0.0126586:0.01183:0.552842:0.867034:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.868342 ES:SE:LP:AF:ID  -0.0114327:0.0118722:0.468521:0.868342:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.868351 ES:SE:LP:AF:ID  -0.0114186:0.0118731:0.468521:0.868351:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.868351 ES:SE:LP:AF:ID  -0.0114263:0.0118728:0.468521:0.868351:rs3131956