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_104240.vcf.gz --id UKB-b:2867 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_104240.txt.gz --cohort_controls 64949 --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-2867/UKB-b-2867_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2867/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:43:04 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-2867/UKB-b-2867_data.vcf.gz ...
Read summary statistics for 8625255 SNPs.
Dropped 7372 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, 1285751 SNPs remain.
After merging with regression SNP LD, 1285751 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0156 (0.0069)
Lambda GC: 1.0373
Mean Chi^2: 1.0355
Intercept: 1.0157 (0.0063)
Ratio: 0.4408 (0.1785)
Analysis finished at Thu Oct 17 14:44:49 2019
Total time elapsed: 1.0m:45.33s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 6.7638e-07,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 1,
    "n_p_sig": 1,
    "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": 83101,
    "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": 1285751,
    "ldsc_nsnp_merge_regression_ld": 1285751,
    "ldsc_observed_scale_h2_beta": 0.0156,
    "ldsc_observed_scale_h2_se": 0.0069,
    "ldsc_intercept_beta": 1.0157,
    "ldsc_intercept_se": 0.0063,
    "ldsc_lambda_gc": 1.0373,
    "ldsc_mean_chisq": 1.0355,
    "ldsc_ratio": 0.4423
}

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 8617917 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 8625255 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 8.650335e+00 5.760993e+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 7.877026e+07 5.637169e+07 828.0000000 3.238547e+07 6.929218e+07 1.145689e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 7.000000e-07 1.796980e-02 -0.1942050 -7.541500e-03 -5.550000e-05 7.465800e-03 1.956120e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.473580e-02 1.005920e-02 0.0058523 6.891600e-03 1.016150e-02 1.991990e-02 1.008890e-01 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.953700e-01 2.896450e-01 0.0000000 2.399999e-01 4.899999e-01 7.499995e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.953708e-01 2.896190e-01 0.0000000 2.433552e-01 4.934508e-01 7.462583e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291107e-01 2.594876e-01 0.0053890 2.518600e-02 1.138890e-01 3.626125e-01 9.946110e-01 ▇▂▁▁▁
numeric AF_reference 83101 0.9903654 NA NA NA NA NA NA NA 2.288600e-01 2.514584e-01 0.0000000 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 -0.0027908 0.0107851 0.8000000 0.7958149 0.623812 0.7821490 NA
1 54676 rs2462492 C T 0.0066173 0.0107537 0.5400003 0.5383248 0.399144 NA NA
1 86028 rs114608975 T C -0.0010673 0.0171182 0.9500000 0.9502846 0.103536 0.0277556 NA
1 91536 rs6702460 G T 0.0068861 0.0105770 0.5199996 0.5150201 0.455916 0.4207270 NA
1 234313 rs8179466 C T -0.0049963 0.0209175 0.8100000 0.8112165 0.074455 NA NA
1 534192 rs6680723 C T 0.0115594 0.0120467 0.3400001 0.3372822 0.242057 NA NA
1 546697 rs12025928 A G 0.0042428 0.0149473 0.7800007 0.7765249 0.912862 NA NA
1 693731 rs12238997 A G 0.0155265 0.0100449 0.1199999 0.1221747 0.117313 0.1417730 NA
1 705882 rs72631875 G A 0.0131766 0.0146415 0.3700002 0.3681475 0.067698 0.0315495 NA
1 706368 rs55727773 A G -0.0102600 0.0074560 0.1700000 0.1688008 0.513304 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0077178 0.0090691 0.3900004 0.3947685 0.136315 0.2052720 NA
22 51219387 rs9616832 T C -0.0115723 0.0118204 0.3300000 0.3275746 0.071797 0.0654952 NA
22 51219704 rs147475742 G A -0.0094258 0.0157243 0.5500004 0.5488793 0.041190 0.0473243 NA
22 51221190 rs369304721 G A -0.0030385 0.0158359 0.8499999 0.8478428 0.048372 NA NA
22 51221731 rs115055839 T C -0.0115059 0.0118230 0.3300000 0.3304641 0.071348 0.0625000 NA
22 51222100 rs114553188 G T -0.0024090 0.0137003 0.8600001 0.8604254 0.054850 0.0880591 NA
22 51223637 rs375798137 G A -0.0023698 0.0137719 0.8600001 0.8633768 0.054470 0.0788738 NA
22 51229805 rs9616985 T C -0.0128079 0.0118595 0.2800000 0.2801557 0.071253 0.0730831 NA
22 51232488 rs376461333 A G -0.0136271 0.0273147 0.6200004 0.6178551 0.020460 NA NA
22 51237063 rs3896457 T C 0.0005371 0.0071596 0.9400001 0.9402056 0.298393 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623812 ES:SE:LP:AF:ID  -0.00279083:0.0107851:0.09691:0.623812:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.399144 ES:SE:LP:AF:ID  0.00661727:0.0107537:0.267606:0.399144:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103536 ES:SE:LP:AF:ID  -0.00106731:0.0171182:0.0222764:0.103536:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455916 ES:SE:LP:AF:ID  0.00688606:0.010577:0.283997:0.455916:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074455 ES:SE:LP:AF:ID  -0.00499629:0.0209175:0.091515:0.074455:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242057 ES:SE:LP:AF:ID  0.0115594:0.0120467:0.468521:0.242057:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912862 ES:SE:LP:AF:ID  0.0042428:0.0149473:0.107905:0.912862:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117313 ES:SE:LP:AF:ID  0.0155265:0.0100449:0.920819:0.117313:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067698 ES:SE:LP:AF:ID  0.0131766:0.0146415:0.431798:0.067698:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513304 ES:SE:LP:AF:ID  -0.01026:0.00745603:0.769551:0.513304:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033677 ES:SE:LP:AF:ID  0.0237864:0.0185845:0.69897:0.033677:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037457 ES:SE:LP:AF:ID  0.0228902:0.0168552:0.769551:0.037457:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037644 ES:SE:LP:AF:ID  0.0227053:0.016767:0.744727:0.037644:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.03722  ES:SE:LP:AF:ID  0.0219085:0.0169202:0.69897:0.03722:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016282 ES:SE:LP:AF:ID  0.0929092:0.0265404:3.33724:0.016282:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.03786  ES:SE:LP:AF:ID  0.0232909:0.0167097:0.79588:0.03786:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037955 ES:SE:LP:AF:ID  0.0206278:0.0166567:0.657577:0.037955:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102736 ES:SE:LP:AF:ID  -0.00904886:0.0121673:0.337242:0.102736:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.95809  ES:SE:LP:AF:ID  -0.0186192:0.0160878:0.60206:0.95809:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.03169  ES:SE:LP:AF:ID  0.0171573:0.0294595:0.251812:0.03169:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052723 ES:SE:LP:AF:ID  0.0295932:0.0237288:0.677781:0.052723:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037449 ES:SE:LP:AF:ID  0.0207189:0.0167661:0.657577:0.037449:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037717 ES:SE:LP:AF:ID  0.0193308:0.0166272:0.619789:0.037717:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841441 ES:SE:LP:AF:ID  -0.0154263:0.00868698:1.11919:0.841441:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056334 ES:SE:LP:AF:ID  -0.00029536:0.0141115:0.00877392:0.056334:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123078 ES:SE:LP:AF:ID  0.015036:0.00954149:0.920819:0.123078:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.02513  ES:SE:LP:AF:ID  -0.0186153:0.0237615:0.366532:0.02513:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.12233  ES:SE:LP:AF:ID  0.0154922:0.00954457:1:0.12233:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134139 ES:SE:LP:AF:ID  0.00709146:0.00936955:0.346787:0.134139:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011558 ES:SE:LP:AF:ID  0.0234601:0.0333935:0.318759:0.011558:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.0061   ES:SE:LP:AF:ID  0.0354864:0.0424056:0.39794:0.0061:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.0376   ES:SE:LP:AF:ID  0.01908:0.0164731:0.60206:0.0376:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837029 ES:SE:LP:AF:ID  -0.0154292:0.0084036:1.18046:0.837029:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836733 ES:SE:LP:AF:ID  -0.015369:0.00839743:1.17393:0.836733:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868562 ES:SE:LP:AF:ID  -0.0142418:0.00902347:0.958607:0.868562:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.131004 ES:SE:LP:AF:ID  0.0141898:0.00904665:0.920819:0.131004:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038045 ES:SE:LP:AF:ID  0.0160311:0.0162147:0.49485:0.038045:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038292 ES:SE:LP:AF:ID  0.0162178:0.0161142:0.508638:0.038292:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867976 ES:SE:LP:AF:ID  -0.0143722:0.00900897:0.958607:0.867976:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.86805  ES:SE:LP:AF:ID  -0.0145333:0.00901265:0.958607:0.86805:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038199 ES:SE:LP:AF:ID  0.0147655:0.0161886:0.443698:0.038199:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867987 ES:SE:LP:AF:ID  -0.0144433:0.00900874:0.958607:0.867987:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005396 ES:SE:LP:AF:ID  -0.0731817:0.0451514:0.958607:0.005396:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836159 ES:SE:LP:AF:ID  -0.0152702:0.00837286:1.16749:0.836159:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038203 ES:SE:LP:AF:ID  0.0143484:0.0162122:0.420216:0.038203:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836793 ES:SE:LP:AF:ID  -0.0152352:0.0083958:1.1549:0.836793:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01303  ES:SE:LP:AF:ID  0.00141268:0.0303751:0.0177288:0.01303:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  0.0258673:0.0450923:0.244125:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838109 ES:SE:LP:AF:ID  -0.0146879:0.00851399:1.07572:0.838109:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868228 ES:SE:LP:AF:ID  -0.0144192:0.00899714:0.958607:0.868228:rs3115858