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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    "FORMAT.1": "<ID=SE,Number=A,Type=Float,Description=\"Standard error of effect size estimate\">",
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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_3894.vcf.gz --id UKB-b:2729 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_3894.txt.gz --cohort_controls 10339 --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-2729/UKB-b-2729_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2729/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:42:52 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2729/UKB-b-2729_data.vcf.gz ...
Read summary statistics for 5996891 SNPs.
Dropped 2738 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, 1214322 SNPs remain.
After merging with regression SNP LD, 1214322 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0927 (0.0487)
Lambda GC: 1.0137
Mean Chi^2: 1.0183
Intercept: 0.9985 (0.0069)
Ratio < 0 (usually indicates GC correction).
Analysis finished at Thu Oct 17 14:44:01 2019
Total time elapsed: 1.0m:9.33s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9243,
    "inflation_factor": 1,
    "mean_EFFECT": -0.0001,
    "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": 54217,
    "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": 1214322,
    "ldsc_nsnp_merge_regression_ld": 1214322,
    "ldsc_observed_scale_h2_beta": 0.0927,
    "ldsc_observed_scale_h2_se": 0.0487,
    "ldsc_intercept_beta": 0.9985,
    "ldsc_intercept_se": 0.0069,
    "ldsc_lambda_gc": 1.0137,
    "ldsc_mean_chisq": 1.0183,
    "ldsc_ratio": -0.082
}

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 5994171 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 5996891 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 8.672163e+00 5.761940e+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 7.857020e+07 5.651491e+07 828.0000000 3.199077e+07 6.901779e+07 1.144706e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -1.201000e-04 2.162770e-02 -0.1736470 -1.295500e-02 -5.930000e-05 1.275980e-02 1.807580e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 2.037220e-02 6.832700e-03 0.0133055 1.475000e-02 1.770990e-02 2.436410e-02 7.092290e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.977473e-01 2.899546e-01 0.0000011 2.500000e-01 5.000000e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.977463e-01 2.899325e-01 0.0000011 2.462350e-01 4.968404e-01 7.488916e-01 9.999995e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 3.118654e-01 2.533704e-01 0.0338530 9.694300e-02 2.298340e-01 4.773080e-01 9.661470e-01 ▇▃▂▂▁
numeric AF_reference 54217 0.9909591 NA NA NA NA NA NA NA 3.083108e-01 2.465159e-01 0.0000000 1.058310e-01 2.374200e-01 4.676520e-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 0.0062794 0.0247862 0.8000000 0.8000050 0.625637 0.7821490 NA
1 54676 rs2462492 C T -0.0125220 0.0244539 0.6100002 0.6086051 0.401793 NA NA
1 86028 rs114608975 T C 0.0630213 0.0392864 0.1100001 0.1086809 0.102815 0.0277556 NA
1 91536 rs6702460 G T -0.0497718 0.0236335 0.0350002 0.0352056 0.458611 0.4207270 NA
1 234313 rs8179466 C T 0.0162005 0.0464680 0.7300002 0.7273613 0.074987 NA NA
1 534192 rs6680723 C T 0.0063885 0.0272531 0.8100000 0.8146638 0.242777 NA NA
1 546697 rs12025928 A G -0.0446709 0.0344103 0.1900002 0.1942241 0.914951 NA NA
1 693731 rs12238997 A G -0.0050029 0.0231571 0.8300000 0.8289549 0.115040 0.1417730 NA
1 705882 rs72631875 G A -0.0080593 0.0344304 0.8100000 0.8149267 0.065039 0.0315495 NA
1 706368 rs55727773 A G -0.0124639 0.0170368 0.4600002 0.4644207 0.513987 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51218377 rs2519461 G C 0.0168242 0.0264955 0.5300002 0.5254393 0.074021 0.0826677 NA
22 51219006 rs28729663 G A 0.0369419 0.0204138 0.0700003 0.0703496 0.138522 0.2052720 NA
22 51219387 rs9616832 T C 0.0170111 0.0265162 0.5199996 0.5211744 0.074299 0.0654952 NA
22 51219704 rs147475742 G A 0.0479891 0.0351521 0.1700000 0.1721951 0.043295 0.0473243 NA
22 51221190 rs369304721 G A 0.0343183 0.0356092 0.3400001 0.3351722 0.050005 NA NA
22 51221731 rs115055839 T C 0.0173898 0.0265672 0.5099998 0.5127517 0.073572 0.0625000 NA
22 51222100 rs114553188 G T 0.0419015 0.0309192 0.1800002 0.1753559 0.054687 0.0880591 NA
22 51223637 rs375798137 G A 0.0453886 0.0310938 0.1400000 0.1443639 0.054248 0.0788738 NA
22 51229805 rs9616985 T C 0.0168485 0.0266070 0.5300002 0.5265798 0.073600 0.0730831 NA
22 51237063 rs3896457 T C -0.0276782 0.0161469 0.0870001 0.0865013 0.305313 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.625637 ES:SE:LP:AF:ID  0.00627935:0.0247862:0.09691:0.625637:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.401793 ES:SE:LP:AF:ID  -0.012522:0.0244539:0.21467:0.401793:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.102815 ES:SE:LP:AF:ID  0.0630213:0.0392864:0.958607:0.102815:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.458611 ES:SE:LP:AF:ID  -0.0497718:0.0236335:1.45593:0.458611:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074987 ES:SE:LP:AF:ID  0.0162005:0.046468:0.136677:0.074987:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242777 ES:SE:LP:AF:ID  0.0063885:0.0272531:0.091515:0.242777:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.914951 ES:SE:LP:AF:ID  -0.0446709:0.0344103:0.721246:0.914951:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.11504  ES:SE:LP:AF:ID  -0.00500291:0.0231571:0.0809219:0.11504:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.065039 ES:SE:LP:AF:ID  -0.0080593:0.0344304:0.091515:0.065039:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513987 ES:SE:LP:AF:ID  -0.0124639:0.0170368:0.337242:0.513987:rs12029736
1   715265  rs12184267  C   T   .   PASS    AF=0.036572 ES:SE:LP:AF:ID  0.0271766:0.0388846:0.318759:0.036572:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.036774 ES:SE:LP:AF:ID  0.0258756:0.0386798:0.30103:0.036774:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.0363   ES:SE:LP:AF:ID  0.033946:0.0390877:0.408935:0.0363:rs12184279
1   720381  rs116801199 G   T   .   PASS    AF=0.036999 ES:SE:LP:AF:ID  0.0274427:0.0385198:0.318759:0.036999:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.036949 ES:SE:LP:AF:ID  0.0290173:0.0384747:0.346787:0.036949:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102247 ES:SE:LP:AF:ID  0.0127825:0.0279123:0.187087:0.102247:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.959272 ES:SE:LP:AF:ID  -0.0285405:0.0370508:0.356547:0.959272:rs2977670
1   725060  rs865924913 A   T   .   PASS    AF=0.05335  ES:SE:LP:AF:ID  -0.0350123:0.0535166:0.29243:0.05335:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.036492 ES:SE:LP:AF:ID  0.0308088:0.0386911:0.366532:0.036492:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.036737 ES:SE:LP:AF:ID  0.0258703:0.0383893:0.30103:0.036737:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.844282 ES:SE:LP:AF:ID  -0.00648109:0.0199621:0.124939:0.844282:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055602 ES:SE:LP:AF:ID  -0.00968701:0.0321924:0.119186:0.055602:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121386 ES:SE:LP:AF:ID  -0.00314956:0.0219436:0.05061:0.121386:rs58276399
1   734349  rs141242758 T   C   .   PASS    AF=0.120671 ES:SE:LP:AF:ID  -0.00157254:0.0219418:0.0268721:0.120671:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.132278 ES:SE:LP:AF:ID  0.00750338:0.0215628:0.136677:0.132278:rs79010578
1   752478  rs146277091 G   A   .   PASS    AF=0.036851 ES:SE:LP:AF:ID  0.0205184:0.0379343:0.229148:0.036851:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.839995 ES:SE:LP:AF:ID  -0.0069143:0.0193544:0.142668:0.839995:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.83954  ES:SE:LP:AF:ID  -0.00482322:0.0193054:0.09691:0.83954:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.871156 ES:SE:LP:AF:ID  -0.00483592:0.0207965:0.0861861:0.871156:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.128479 ES:SE:LP:AF:ID  0.000150059:0.0208425:0.00436481:0.128479:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.037189 ES:SE:LP:AF:ID  0.0222159:0.0374274:0.259637:0.037189:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.037431 ES:SE:LP:AF:ID  0.022623:0.0371754:0.267606:0.037431:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.870568 ES:SE:LP:AF:ID  -0.00218067:0.0207423:0.0362122:0.870568:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.870567 ES:SE:LP:AF:ID  -0.0022021:0.0207429:0.0362122:0.870567:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.037469 ES:SE:LP:AF:ID  0.0220788:0.0373297:0.259637:0.037469:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.870517 ES:SE:LP:AF:ID  -0.00340593:0.0207388:0.0604807:0.870517:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.839273 ES:SE:LP:AF:ID  -0.004858:0.0192864:0.09691:0.839273:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.0375   ES:SE:LP:AF:ID  0.0216232:0.0373648:0.251812:0.0375:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.839832 ES:SE:LP:AF:ID  -0.00500163:0.0193316:0.09691:0.839832:rs3131966
1   755775  rs3131965   A   G   .   PASS    AF=0.841304 ES:SE:LP:AF:ID  -0.00560855:0.0196446:0.107905:0.841304:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.8709   ES:SE:LP:AF:ID  -0.00411952:0.0207352:0.0757207:0.8709:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.870345 ES:SE:LP:AF:ID  -0.00584689:0.0206537:0.107905:0.870345:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.869405 ES:SE:LP:AF:ID  -0.0029827:0.0206121:0.0555173:0.869405:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.87069  ES:SE:LP:AF:ID  -0.00440469:0.0207017:0.0809219:0.87069:rs4951929
1   757936  rs4951862   C   A   .   PASS    AF=0.870706 ES:SE:LP:AF:ID  -0.00442086:0.0207036:0.0809219:0.870706:rs4951862
1   758144  rs3131956   A   G   .   PASS    AF=0.870725 ES:SE:LP:AF:ID  -0.00425468:0.0207051:0.0757207:0.870725:rs3131956
1   758626  rs3131954   C   T   .   PASS    AF=0.871165 ES:SE:LP:AF:ID  -0.00325185:0.0207682:0.0555173:0.871165:rs3131954
1   759036  rs114525117 G   A   .   PASS    AF=0.037721 ES:SE:LP:AF:ID  0.0155564:0.0370431:0.173925:0.037721:rs114525117
1   760912  rs1048488   C   T   .   PASS    AF=0.839722 ES:SE:LP:AF:ID  -0.00638059:0.0192776:0.130768:0.839722:rs1048488
1   760998  rs148828841 C   A   .   PASS    AF=0.036981 ES:SE:LP:AF:ID  0.0182756:0.0378358:0.200659:0.036981:rs148828841