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_102370.vcf.gz --id UKB-b:18788 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_102370.txt.gz --cohort_controls 64944 --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-18788/UKB-b-18788_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-18788/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-18788/UKB-b-18788_data.vcf.gz ...
Read summary statistics for 8624983 SNPs.
Dropped 7371 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, 1285749 SNPs remain.
After merging with regression SNP LD, 1285749 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0041 (0.0069)
Lambda GC: 1.0097
Mean Chi^2: 1.0101
Intercept: 1.0049 (0.0063)
Ratio: 0.481 (0.6216)
Analysis finished at Thu Oct 17 14:41:52 2019
Total time elapsed: 1.0m:33.53s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1,
    "mean_EFFECT": -0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 5,
    "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": 83095,
    "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": 1285749,
    "ldsc_nsnp_merge_regression_ld": 1285749,
    "ldsc_observed_scale_h2_beta": 0.0041,
    "ldsc_observed_scale_h2_se": 0.0069,
    "ldsc_intercept_beta": 1.0049,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.0097,
    "ldsc_mean_chisq": 1.0101,
    "ldsc_ratio": 0.4851
}

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 8617646 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 8624983 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 8.650337e+00 5.760986e+00 1.000000 4.000000e+00 8.000000e+00 1.300000e+01 2.200000e+01 ▇▅▅▂▂
numeric POS 0 1.0000000 NA NA NA NA NA NA NA 7.877017e+07 5.637171e+07 828.000000 3.238543e+07 6.929183e+07 1.145689e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA -2.150000e-05 1.202850e-02 -0.127345 -5.044100e-03 -2.960000e-05 4.952000e-03 1.340520e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 9.927900e-03 6.776800e-03 0.003943 4.643400e-03 6.846300e-03 1.342040e-02 6.797250e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.989010e-01 2.889955e-01 0.000000 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 4.989038e-01 2.889706e-01 0.000000 2.480151e-01 4.986942e-01 7.491510e-01 9.999997e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291174e-01 2.594883e-01 0.005390 2.519000e-02 1.138970e-01 3.626260e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83095 0.9903658 NA NA NA NA NA NA NA 2.288666e-01 2.514591e-01 0.000000 2.316290e-02 1.303910e-01 3.596250e-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 -0.0070771 0.0072666 0.3300000 0.3300914 0.623809 0.7821490 NA
1 54676 rs2462492 C T 0.0097708 0.0072455 0.1800002 0.1774874 0.399139 NA NA
1 86028 rs114608975 T C 0.0082867 0.0115343 0.4700002 0.4724856 0.103533 0.0277556 NA
1 91536 rs6702460 G T 0.0024980 0.0071264 0.7300002 0.7259389 0.455908 0.4207270 NA
1 234313 rs8179466 C T 0.0111660 0.0140949 0.4299995 0.4282432 0.074448 NA NA
1 534192 rs6680723 C T -0.0167124 0.0081164 0.0389996 0.0394863 0.242068 NA NA
1 546697 rs12025928 A G 0.0061674 0.0100705 0.5400003 0.5402590 0.912858 NA NA
1 693731 rs12238997 A G -0.0085711 0.0067676 0.2099999 0.2053364 0.117322 0.1417730 NA
1 705882 rs72631875 G A -0.0037546 0.0098645 0.6999999 0.7034896 0.067699 0.0315495 NA
1 706368 rs55727773 A G 0.0000264 0.0050234 1.0000000 0.9958130 0.513300 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0080548 0.0061109 0.1900002 0.1874716 0.136294 0.2052720 NA
22 51219387 rs9616832 T C 0.0043241 0.0079641 0.5900000 0.5871627 0.071795 0.0654952 NA
22 51219704 rs147475742 G A 0.0004362 0.0105948 0.9699999 0.9671581 0.041186 0.0473243 NA
22 51221190 rs369304721 G A 0.0010303 0.0106697 0.9199999 0.9230755 0.048370 NA NA
22 51221731 rs115055839 T C 0.0037719 0.0079658 0.6400000 0.6358480 0.071346 0.0625000 NA
22 51222100 rs114553188 G T 0.0131167 0.0092334 0.1600000 0.1554402 0.054832 0.0880591 NA
22 51223637 rs375798137 G A 0.0126542 0.0092815 0.1700000 0.1727627 0.054452 0.0788738 NA
22 51229805 rs9616985 T C 0.0039973 0.0079904 0.6200004 0.6168949 0.071251 0.0730831 NA
22 51232488 rs376461333 A G 0.0167393 0.0184048 0.3599996 0.3630824 0.020455 NA NA
22 51237063 rs3896457 T C -0.0001205 0.0048238 0.9800000 0.9800720 0.298401 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623809 ES:SE:LP:AF:ID  -0.00707711:0.00726655:0.481486:0.623809:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399139 ES:SE:LP:AF:ID  0.00977076:0.00724548:0.744727:0.399139:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103533 ES:SE:LP:AF:ID  0.00828671:0.0115343:0.327902:0.103533:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455908 ES:SE:LP:AF:ID  0.00249804:0.00712641:0.136677:0.455908:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074448 ES:SE:LP:AF:ID  0.011166:0.0140949:0.366532:0.074448:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242068 ES:SE:LP:AF:ID  -0.0167124:0.00811643:1.40894:0.242068:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912858 ES:SE:LP:AF:ID  0.00616739:0.0100705:0.267606:0.912858:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117322 ES:SE:LP:AF:ID  -0.00857111:0.00676759:0.677781:0.117322:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067699 ES:SE:LP:AF:ID  -0.00375456:0.00986448:0.154902:0.067699:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.5133   ES:SE:LP:AF:ID  2.63609e-05:0.00502343:-0:0.5133:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033666 ES:SE:LP:AF:ID  -0.000324612:0.0125235:0.00877392:0.033666:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037445 ES:SE:LP:AF:ID  0.000146953:0.011358:0.00436481:0.037445:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037632 ES:SE:LP:AF:ID  0.000301109:0.0112986:0.00877392:0.037632:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037208 ES:SE:LP:AF:ID  -0.000402298:0.0114018:0.0132283:0.037208:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016283 ES:SE:LP:AF:ID  0.019392:0.017881:0.552842:0.016283:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037848 ES:SE:LP:AF:ID  0.00102078:0.01126:0.0315171:0.037848:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037942 ES:SE:LP:AF:ID  -1.17631e-06:0.0112242:-0:0.037942:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102724 ES:SE:LP:AF:ID  0.00354394:0.00819814:0.173925:0.102724:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958102 ES:SE:LP:AF:ID  0.0044827:0.0108408:0.167491:0.958102:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031689 ES:SE:LP:AF:ID  -0.0179328:0.0198486:0.431798:0.031689:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052723 ES:SE:LP:AF:ID  0.0114554:0.0159871:0.327902:0.052723:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037436 ES:SE:LP:AF:ID  -0.000543961:0.0112979:0.0177288:0.037436:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037705 ES:SE:LP:AF:ID  0.00032897:0.0112044:0.00877392:0.037705:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841444 ES:SE:LP:AF:ID  0.00785797:0.00585287:0.744727:0.841444:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056338 ES:SE:LP:AF:ID  -0.0138039:0.00950735:0.823909:0.056338:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123087 ES:SE:LP:AF:ID  -0.00699127:0.00642843:0.552842:0.123087:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025132 ES:SE:LP:AF:ID  -0.025319:0.0160088:0.958607:0.025132:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.12234  ES:SE:LP:AF:ID  -0.00688085:0.0064305:0.552842:0.12234:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134134 ES:SE:LP:AF:ID  -0.0087824:0.0063128:0.79588:0.134134:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011558 ES:SE:LP:AF:ID  0.0197366:0.0224981:0.420216:0.011558:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  0.00748738:0.0285698:0.102373:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037587 ES:SE:LP:AF:ID  0.00168444:0.0111005:0.0555173:0.037587:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837032 ES:SE:LP:AF:ID  0.00710408:0.00566193:0.677781:0.837032:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836736 ES:SE:LP:AF:ID  0.00742752:0.00565778:0.721246:0.836736:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868552 ES:SE:LP:AF:ID  0.0074385:0.00607942:0.657577:0.868552:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131014 ES:SE:LP:AF:ID  -0.00776636:0.00609504:0.69897:0.131014:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038033 ES:SE:LP:AF:ID  -0.000536454:0.0109263:0.0177288:0.038033:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038279 ES:SE:LP:AF:ID  -0.000104814:0.0108586:0.00436481:0.038279:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867966 ES:SE:LP:AF:ID  0.0077462:0.00606965:0.69897:0.867966:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86804  ES:SE:LP:AF:ID  0.0077071:0.00607213:0.69897:0.86804:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038187 ES:SE:LP:AF:ID  -0.000625712:0.0109087:0.0222764:0.038187:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867977 ES:SE:LP:AF:ID  0.00774047:0.0060695:0.69897:0.867977:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  -0.00547547:0.0304197:0.0655015:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836162 ES:SE:LP:AF:ID  0.00781147:0.00564122:0.769551:0.836162:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.03819  ES:SE:LP:AF:ID  -0.000876463:0.0109246:0.0268721:0.03819:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836796 ES:SE:LP:AF:ID  0.00773392:0.00565667:0.769551:0.836796:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.013031 ES:SE:LP:AF:ID  0.000352393:0.0204645:0.00436481:0.013031:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  -0.040178:0.0303798:0.721246:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838112 ES:SE:LP:AF:ID  0.00835966:0.00573629:0.823909:0.838112:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868218 ES:SE:LP:AF:ID  0.00752341:0.00606168:0.677781:0.868218:rs3115858