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_104100.vcf.gz --id UKB-b:12236 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_104100.txt.gz --cohort_controls 64946 --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-12236/UKB-b-12236_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12236/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:41:27 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-12236/UKB-b-12236_data.vcf.gz ...
Read summary statistics for 8624952 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, 1285748 SNPs remain.
After merging with regression SNP LD, 1285748 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0077 (0.0071)
Lambda GC: 1.0273
Mean Chi^2: 1.0283
Intercept: 1.0183 (0.0064)
Ratio: 0.6462 (0.2257)
Analysis finished at Thu Oct 17 14:42:59 2019
Total time elapsed: 1.0m:31.86s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 0,
    "n": "-Inf",
    "n_snps": 9851866,
    "n_clumped_hits": 2,
    "n_p_sig": 2,
    "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": 83094,
    "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": 1285748,
    "ldsc_nsnp_merge_regression_ld": 1285748,
    "ldsc_observed_scale_h2_beta": 0.0077,
    "ldsc_observed_scale_h2_se": 0.0071,
    "ldsc_intercept_beta": 1.0183,
    "ldsc_intercept_se": 0.0064,
    "ldsc_lambda_gc": 1.0273,
    "ldsc_mean_chisq": 1.0283,
    "ldsc_ratio": 0.6466
}

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 TRUE
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 8617615 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 8624952 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 8.650351e+00 5.760983e+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 7.877003e+07 5.637162e+07 828.0000000 3.238541e+07 6.929178e+07 1.145686e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 3.450000e-05 6.603600e-03 -0.0761337 -2.756300e-03 -1.890000e-05 2.722500e-03 7.984330e-02 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 5.384800e-03 3.675600e-03 0.0021386 2.518500e-03 3.713300e-03 7.279000e-03 3.686560e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.957429e-01 2.898923e-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 4.957440e-01 2.898666e-01 0.0000000 2.433847e-01 4.943487e-01 7.469213e-01 9.999998e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.291182e-01 2.594884e-01 0.0053900 2.519000e-02 1.138980e-01 3.626260e-01 9.946100e-01 ▇▂▁▁▁
numeric AF_reference 83094 0.9903659 NA NA NA NA NA NA NA 2.288672e-01 2.514592e-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 -0.0045331 0.0039414 0.2500000 0.2500963 0.623815 0.7821490 NA
1 54676 rs2462492 C T 0.0006302 0.0039300 0.8700001 0.8725962 0.399140 NA NA
1 86028 rs114608975 T C 0.0112977 0.0062561 0.0710003 0.0709387 0.103532 0.0277556 NA
1 91536 rs6702460 G T 0.0033743 0.0038654 0.3800004 0.3826947 0.455916 0.4207270 NA
1 234313 rs8179466 C T 0.0016275 0.0076446 0.8300000 0.8314080 0.074451 NA NA
1 534192 rs6680723 C T 0.0056184 0.0044025 0.2000000 0.2018848 0.242057 NA NA
1 546697 rs12025928 A G -0.0005385 0.0054626 0.9199999 0.9214771 0.912866 NA NA
1 693731 rs12238997 A G -0.0059227 0.0036709 0.1100001 0.1066558 0.117319 0.1417730 NA
1 705882 rs72631875 G A 0.0047138 0.0053508 0.3800004 0.3783436 0.067697 0.0315495 NA
1 706368 rs55727773 A G 0.0014117 0.0027248 0.5999997 0.6043986 0.513298 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A 0.0021414 0.0033144 0.5199996 0.5182233 0.136313 0.2052720 NA
22 51219387 rs9616832 T C -0.0014422 0.0043198 0.7400005 0.7384913 0.071801 0.0654952 NA
22 51219704 rs147475742 G A -0.0063493 0.0057465 0.2700001 0.2691980 0.041192 0.0473243 NA
22 51221190 rs369304721 G A -0.0015529 0.0057872 0.7899998 0.7884383 0.048375 NA NA
22 51221731 rs115055839 T C -0.0011997 0.0043207 0.7800007 0.7812710 0.071351 0.0625000 NA
22 51222100 rs114553188 G T 0.0088868 0.0050071 0.0759994 0.0759259 0.054845 0.0880591 NA
22 51223637 rs375798137 G A 0.0087025 0.0050333 0.0840001 0.0838076 0.054465 0.0788738 NA
22 51229805 rs9616985 T C -0.0016879 0.0043341 0.6999999 0.6969405 0.071256 0.0730831 NA
22 51232488 rs376461333 A G 0.0104609 0.0099822 0.2900000 0.2946592 0.020460 NA NA
22 51237063 rs3896457 T C -0.0003471 0.0026166 0.8900000 0.8944768 0.298392 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623815 ES:SE:LP:AF:ID  -0.0045331:0.00394143:0.60206:0.623815:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.39914  ES:SE:LP:AF:ID  0.000630218:0.00392998:0.0604807:0.39914:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103532 ES:SE:LP:AF:ID  0.0112977:0.0062561:1.14874:0.103532:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455916 ES:SE:LP:AF:ID  0.00337427:0.0038654:0.420216:0.455916:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074451 ES:SE:LP:AF:ID  0.00162751:0.00764462:0.0809219:0.074451:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242057 ES:SE:LP:AF:ID  0.00561842:0.00440246:0.69897:0.242057:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912866 ES:SE:LP:AF:ID  -0.000538464:0.00546258:0.0362122:0.912866:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117319 ES:SE:LP:AF:ID  -0.00592271:0.00367093:0.958607:0.117319:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067697 ES:SE:LP:AF:ID  0.0047138:0.0053508:0.420216:0.067697:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513298 ES:SE:LP:AF:ID  0.0014117:0.00272484:0.221849:0.513298:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033678 ES:SE:LP:AF:ID  -0.00223209:0.00679169:0.130768:0.033678:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.037459 ES:SE:LP:AF:ID  -0.00390756:0.00615973:0.275724:0.037459:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037645 ES:SE:LP:AF:ID  -0.00352367:0.0061275:0.244125:0.037645:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037222 ES:SE:LP:AF:ID  -0.00381891:0.00618349:0.267606:0.037222:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016283 ES:SE:LP:AF:ID  -0.00952141:0.0096992:0.481486:0.016283:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037862 ES:SE:LP:AF:ID  -0.00324784:0.00610655:0.229148:0.037862:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037956 ES:SE:LP:AF:ID  -0.00366722:0.00608718:0.259637:0.037956:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102737 ES:SE:LP:AF:ID  -0.00271882:0.00444654:0.267606:0.102737:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958088 ES:SE:LP:AF:ID  0.00215743:0.0058793:0.148742:0.958088:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031691 ES:SE:LP:AF:ID  -0.00196919:0.010766:0.0705811:0.031691:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052723 ES:SE:LP:AF:ID  0.00955908:0.00867169:0.568636:0.052723:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.03745  ES:SE:LP:AF:ID  -0.00313266:0.00612715:0.21467:0.03745:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.037719 ES:SE:LP:AF:ID  -0.0029123:0.0060764:0.200659:0.037719:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841434 ES:SE:LP:AF:ID  0.00381312:0.00317468:0.638272:0.841434:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056337 ES:SE:LP:AF:ID  -0.00990767:0.00515706:1.25964:0.056337:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123083 ES:SE:LP:AF:ID  -0.00383154:0.00348695:0.568636:0.123083:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.025131 ES:SE:LP:AF:ID  -0.00651509:0.00868365:0.346787:0.025131:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122336 ES:SE:LP:AF:ID  -0.00374092:0.00348808:0.552842:0.122336:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134146 ES:SE:LP:AF:ID  -0.00351952:0.00342412:0.522879:0.134146:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011558 ES:SE:LP:AF:ID  0.00390425:0.0122036:0.124939:0.011558:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006101 ES:SE:LP:AF:ID  0.00115102:0.0154971:0.0268721:0.006101:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037601 ES:SE:LP:AF:ID  -0.00302077:0.00602009:0.207608:0.037601:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837022 ES:SE:LP:AF:ID  0.00526191:0.00307112:1.06048:0.837022:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836726 ES:SE:LP:AF:ID  0.00534039:0.00306886:1.08619:0.836726:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868556 ES:SE:LP:AF:ID  0.00506736:0.00329765:0.920819:0.868556:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.13101  ES:SE:LP:AF:ID  -0.00517858:0.00330611:0.920819:0.13101:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038047 ES:SE:LP:AF:ID  -0.00315146:0.00592566:0.229148:0.038047:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038293 ES:SE:LP:AF:ID  -0.00334691:0.00588893:0.244125:0.038293:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.86797  ES:SE:LP:AF:ID  0.00519106:0.00329234:0.958607:0.86797:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868044 ES:SE:LP:AF:ID  0.00516017:0.00329369:0.920819:0.868044:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038201 ES:SE:LP:AF:ID  -0.00338998:0.00591613:0.244125:0.038201:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867981 ES:SE:LP:AF:ID  0.00518617:0.00329226:0.920819:0.867981:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005397 ES:SE:LP:AF:ID  -0.0177227:0.0165006:0.552842:0.005397:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836152 ES:SE:LP:AF:ID  0.00535127:0.00305989:1.09691:0.836152:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038204 ES:SE:LP:AF:ID  -0.00327454:0.00592476:0.236572:0.038204:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836785 ES:SE:LP:AF:ID  0.00533718:0.00306827:1.08619:0.836785:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01303  ES:SE:LP:AF:ID  0.016761:0.0111006:0.886057:0.01303:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005662 ES:SE:LP:AF:ID  0.00542579:0.0164789:0.130768:0.005662:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838102 ES:SE:LP:AF:ID  0.0052059:0.00311146:1.02687:0.838102:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868222 ES:SE:LP:AF:ID  0.0052154:0.00328802:0.958607:0.868222:rs3115858