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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    "FORMAT.5": "<ID=EZ,Number=A,Type=Float,Description=\"Z-score provided if it was used to derive the EFFECT and SE fields\">",
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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_2824.vcf.gz --id UKB-b:4643 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_2824.txt.gz --cohort_controls 46207 --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-4643/UKB-b-4643_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4643/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:44:33 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-4643/UKB-b-4643_data.vcf.gz ...
Read summary statistics for 8126099 SNPs.
Dropped 6351 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, 1283209 SNPs remain.
After merging with regression SNP LD, 1283209 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0987 (0.0206)
Lambda GC: 1.0935
Mean Chi^2: 1.1086
Intercept: 1.0141 (0.0095)
Ratio: 0.1297 (0.0871)
Analysis finished at Thu Oct 17 14:46:02 2019
Total time elapsed: 1.0m:28.61s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9436,
    "inflation_factor": 1.0966,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 4,
    "n_p_sig": 141,
    "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": 76039,
    "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": 1283209,
    "ldsc_nsnp_merge_regression_ld": 1283209,
    "ldsc_observed_scale_h2_beta": 0.0987,
    "ldsc_observed_scale_h2_se": 0.0206,
    "ldsc_intercept_beta": 1.0141,
    "ldsc_intercept_se": 0.0095,
    "ldsc_lambda_gc": 1.0935,
    "ldsc_mean_chisq": 1.1086,
    "ldsc_ratio": 0.1298
}

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 8119776 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 8126099 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 8.659135e+00 5.762946e+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 7.871031e+07 5.639374e+07 828.0000000 3.229604e+07 6.918826e+07 1.145299e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -4.780000e-05 1.724370e-02 -0.2298090 -7.930700e-03 -1.370000e-05 7.880000e-03 1.654390e-01 ▁▁▇▃▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.435690e-02 8.880100e-03 0.0063303 7.384400e-03 1.039940e-02 1.899570e-02 7.702170e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.881559e-01 2.920833e-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 4.881544e-01 2.920580e-01 0.0000000 2.326612e-01 4.843385e-01 7.412341e-01 1.000000e+00 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.420492e-01 2.605733e-01 0.0075750 3.308000e-02 1.315830e-01 3.835915e-01 9.924250e-01 ▇▂▂▁▁
numeric AF_reference 76039 0.9906426 NA NA NA NA NA NA NA 2.413983e-01 2.524229e-01 0.0000000 3.394570e-02 1.473640e-01 3.789940e-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 -0.0242732 0.0117113 0.0379997 0.0382067 0.623875 0.7821490 NA
1 54676 rs2462492 C T 0.0085241 0.0115789 0.4600002 0.4616225 0.401541 NA NA
1 86028 rs114608975 T C 0.0236306 0.0188376 0.2099999 0.2096829 0.102309 0.0277556 NA
1 91536 rs6702460 G T -0.0059354 0.0113879 0.5999997 0.6022228 0.458302 0.4207270 NA
1 234313 rs8179466 C T -0.0122634 0.0224489 0.5800000 0.5848730 0.074407 NA NA
1 534192 rs6680723 C T 0.0216622 0.0130897 0.0980009 0.0979439 0.239387 NA NA
1 546697 rs12025928 A G 0.0081755 0.0161700 0.6100002 0.6131406 0.913612 NA NA
1 693731 rs12238997 A G 0.0181331 0.0109616 0.0980009 0.0980791 0.115752 0.1417730 NA
1 705882 rs72631875 G A -0.0047393 0.0160149 0.7700005 0.7672826 0.066790 0.0315495 NA
1 706368 rs55727773 A G -0.0049787 0.0080924 0.5400003 0.5384017 0.514869 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0171958 0.0097342 0.0769999 0.0773047 0.138323 0.2052720 NA
22 51219387 rs9616832 T C 0.0084645 0.0125975 0.5000000 0.5016352 0.074414 0.0654952 NA
22 51219704 rs147475742 G A 0.0198348 0.0170002 0.2399999 0.2433157 0.041905 0.0473243 NA
22 51221190 rs369304721 G A 0.0262105 0.0168480 0.1199999 0.1197785 0.050225 NA NA
22 51221731 rs115055839 T C 0.0080780 0.0125962 0.5199996 0.5213249 0.073928 0.0625000 NA
22 51222100 rs114553188 G T 0.0262598 0.0149291 0.0790005 0.0785830 0.054317 0.0880591 NA
22 51223637 rs375798137 G A 0.0262387 0.0150026 0.0800000 0.0803008 0.053964 0.0788738 NA
22 51229805 rs9616985 T C 0.0087677 0.0126451 0.4899999 0.4880785 0.073719 0.0730831 NA
22 51232488 rs376461333 A G 0.0340789 0.0307314 0.2700001 0.2674614 0.019528 NA NA
22 51237063 rs3896457 T C -0.0017367 0.0077674 0.8200001 0.8230739 0.300342 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623875 ES:SE:LP:AF:ID  -0.0242732:0.0117113:1.42022:0.623875:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.401541 ES:SE:LP:AF:ID  0.00852413:0.0115789:0.337242:0.401541:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.102309 ES:SE:LP:AF:ID  0.0236306:0.0188376:0.677781:0.102309:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.458302 ES:SE:LP:AF:ID  -0.00593545:0.0113879:0.221849:0.458302:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074407 ES:SE:LP:AF:ID  -0.0122634:0.0224489:0.236572:0.074407:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.239387 ES:SE:LP:AF:ID  0.0216622:0.0130897:1.00877:0.239387:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.913612 ES:SE:LP:AF:ID  0.00817548:0.01617:0.21467:0.913612:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.115752 ES:SE:LP:AF:ID  0.0181331:0.0109616:1.00877:0.115752:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.06679  ES:SE:LP:AF:ID  -0.00473931:0.0160149:0.113509:0.06679:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.514869 ES:SE:LP:AF:ID  -0.00497872:0.00809243:0.267606:0.514869:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.031487 ES:SE:LP:AF:ID  -0.00939197:0.0208217:0.187087:0.031487:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.034935 ES:SE:LP:AF:ID  -0.00703411:0.0189375:0.148742:0.034935:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.035044 ES:SE:LP:AF:ID  -0.00596238:0.0188597:0.124939:0.035044:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.03476  ES:SE:LP:AF:ID  -0.0069154:0.0189956:0.142668:0.03476:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.015869 ES:SE:LP:AF:ID  0.00538392:0.0291356:0.0705811:0.015869:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.035296 ES:SE:LP:AF:ID  -0.00763247:0.0187796:0.167491:0.035296:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.035363 ES:SE:LP:AF:ID  -0.00690762:0.01872:0.148742:0.035363:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102466 ES:SE:LP:AF:ID  -0.00954745:0.0132147:0.327902:0.102466:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.961057 ES:SE:LP:AF:ID  0.00455154:0.0180716:0.09691:0.961057:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031567 ES:SE:LP:AF:ID  0.0137981:0.031939:0.173925:0.031567:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.054133 ES:SE:LP:AF:ID  -0.0239919:0.0251225:0.468521:0.054133:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.034956 ES:SE:LP:AF:ID  -0.00765543:0.0188264:0.167491:0.034956:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.035284 ES:SE:LP:AF:ID  -0.00705036:0.0186523:0.148742:0.035284:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.845857 ES:SE:LP:AF:ID  -0.0148807:0.00955319:0.920819:0.845857:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.055659 ES:SE:LP:AF:ID  0.0119721:0.0153672:0.356547:0.055659:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.121539 ES:SE:LP:AF:ID  0.0159848:0.0104117:0.920819:0.121539:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025786 ES:SE:LP:AF:ID  -0.0249861:0.0254834:0.481486:0.025786:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.120783 ES:SE:LP:AF:ID  0.0164805:0.0104166:0.958607:0.120783:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.130808 ES:SE:LP:AF:ID  0.0162509:0.0102771:0.958607:0.130808:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.010799 ES:SE:LP:AF:ID  0.0459527:0.0374279:0.657577:0.010799:rs181876450
1   752478  rs146277091 G   A   .   PASS    AF=0.035117 ES:SE:LP:AF:ID  -0.00587615:0.0184878:0.124939:0.035117:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.840825 ES:SE:LP:AF:ID  -0.0129432:0.00922512:0.79588:0.840825:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.840519 ES:SE:LP:AF:ID  -0.0127062:0.00921655:0.769551:0.840519:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.870206 ES:SE:LP:AF:ID  -0.0146772:0.00986395:0.853872:0.870206:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.129431 ES:SE:LP:AF:ID  0.014481:0.00988327:0.853872:0.129431:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.035624 ES:SE:LP:AF:ID  -0.00796848:0.0181681:0.180456:0.035624:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.035904 ES:SE:LP:AF:ID  -0.00813247:0.0180333:0.187087:0.035904:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.869604 ES:SE:LP:AF:ID  -0.0145317:0.0098461:0.853872:0.869604:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.869646 ES:SE:LP:AF:ID  -0.014698:0.00984727:0.853872:0.869646:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.035835 ES:SE:LP:AF:ID  -0.00853548:0.0181249:0.19382:0.035835:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.869608 ES:SE:LP:AF:ID  -0.0143396:0.00984573:0.823909:0.869608:rs3131967
1   754503  rs3115859   G   A   .   PASS    AF=0.84001  ES:SE:LP:AF:ID  -0.0130765:0.00919225:0.823909:0.84001:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.035864 ES:SE:LP:AF:ID  -0.00841427:0.0181444:0.19382:0.035864:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.840604 ES:SE:LP:AF:ID  -0.0133926:0.00921705:0.823909:0.840604:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013973 ES:SE:LP:AF:ID  -0.0478645:0.0316684:0.886057:0.013973:rs181660517
1   755775  rs3131965   A   G   .   PASS    AF=0.841533 ES:SE:LP:AF:ID  -0.0140649:0.00933887:0.886057:0.841533:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.86973  ES:SE:LP:AF:ID  -0.0141634:0.00983061:0.823909:0.86973:rs3115858
1   756604  rs3131962   A   G   .   PASS    AF=0.869259 ES:SE:LP:AF:ID  -0.0139496:0.00980494:0.823909:0.869259:rs3131962
1   757640  rs3115853   G   A   .   PASS    AF=0.86829  ES:SE:LP:AF:ID  -0.0125434:0.00978629:0.69897:0.86829:rs3115853
1   757734  rs4951929   C   T   .   PASS    AF=0.869407 ES:SE:LP:AF:ID  -0.0135684:0.00981309:0.769551:0.869407:rs4951929