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_100025.vcf.gz --id UKB-b:6888 --data /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/ukbiobank/raw-output/data.batch_100025.txt.gz --cohort_controls 64979 --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-6888/UKB-b-6888_data.vcf.gz \
--ref-ld-chr ../reference/eur_w_ld_chr/ \
--out /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6888/ldsc.txt \
--w-ld-chr ../reference/eur_w_ld_chr/ 

Beginning analysis at Thu Oct 17 14:40:17 2019
Reading summary statistics from /mnt/storage/private/mrcieu/research/scratch/IGD/data/public/UKB-b-6888/UKB-b-6888_data.vcf.gz ...
Read summary statistics for 8625788 SNPs.
Dropped 7373 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, 1285750 SNPs remain.
After merging with regression SNP LD, 1285750 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0345 (0.0073)
Lambda GC: 1.0588
Mean Chi^2: 1.0554
Intercept: 1.0123 (0.0061)
Ratio: 0.2218 (0.1099)
Analysis finished at Thu Oct 17 14:41:45 2019
Total time elapsed: 1.0m:27.82s

QC metrics

Metrics

Metrics

{
    "af_correlation": 0.9462,
    "inflation_factor": 1.0475,
    "mean_EFFECT": 0,
    "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": 83109,
    "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": 1285750,
    "ldsc_nsnp_merge_regression_ld": 1285750,
    "ldsc_observed_scale_h2_beta": 0.0345,
    "ldsc_observed_scale_h2_se": 0.0073,
    "ldsc_intercept_beta": 1.0123,
    "ldsc_intercept_se": 0.0061,
    "ldsc_lambda_gc": 1.0588,
    "ldsc_mean_chisq": 1.0554,
    "ldsc_ratio": 0.222
}

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 8618449 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 8625788 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 8.650298e+00 5.760984e+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 7.877027e+07 5.637184e+07 828.0000000 3.238558e+07 6.929189e+07 1.145689e+08 2.492385e+08 ▇▆▅▂▁
numeric EFFECT 0 1.0000000 NA NA NA NA NA NA NA 2.810000e-05 1.629230e-02 -0.1735250 -6.823400e-03 3.710000e-05 6.847200e-03 1.937920e-01 ▁▁▇▁▁
numeric SE 0 1.0000000 NA NA NA NA NA NA NA 1.331170e-02 9.088000e-03 0.0052863 6.225000e-03 9.179100e-03 1.799580e-02 9.114160e-02 ▇▂▁▁▁
numeric PVAL 0 1.0000000 NA NA NA NA NA NA NA 4.932211e-01 2.900667e-01 0.0000002 2.399999e-01 4.899999e-01 7.400005e-01 1.000000e+00 ▇▇▇▇▇
numeric PVAL_ztest 0 1.0000000 NA NA NA NA NA NA NA 4.932207e-01 2.900401e-01 0.0000002 2.408326e-01 4.904938e-01 7.442993e-01 9.999999e-01 ▇▇▇▇▇
numeric AF 0 1.0000000 NA NA NA NA NA NA NA 2.290973e-01 2.594859e-01 0.0053870 2.517900e-02 1.138710e-01 3.625850e-01 9.946120e-01 ▇▂▁▁▁
numeric AF_reference 83109 0.9903651 NA NA NA NA NA NA NA 2.288473e-01 2.514568e-01 0.0000000 2.316290e-02 1.303910e-01 3.594250e-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 -0.0020157 0.0097393 0.8400000 0.8360380 0.623798 0.7821490 NA
1 54676 rs2462492 C T -0.0094763 0.0097108 0.3300000 0.3291370 0.399160 NA NA
1 86028 rs114608975 T C -0.0069388 0.0154588 0.6499995 0.6535330 0.103537 0.0277556 NA
1 91536 rs6702460 G T -0.0027564 0.0095523 0.7700005 0.7729181 0.455943 0.4207270 NA
1 234313 rs8179466 C T -0.0103679 0.0188929 0.5800000 0.5831617 0.074448 NA NA
1 534192 rs6680723 C T 0.0029543 0.0108789 0.7899998 0.7859571 0.242084 NA NA
1 546697 rs12025928 A G 0.0002531 0.0135002 0.9900000 0.9850429 0.912869 NA NA
1 693731 rs12238997 A G -0.0124141 0.0090724 0.1700000 0.1712066 0.117299 0.1417730 NA
1 705882 rs72631875 G A 0.0301009 0.0132241 0.0230001 0.0228331 0.067692 0.0315495 NA
1 706368 rs55727773 A G 0.0063697 0.0067337 0.3400001 0.3441818 0.513316 0.2751600 NA
CHROM POS ID REF ALT EFFECT SE PVAL PVAL_ztest AF AF_reference N
22 51219006 rs28729663 G A -0.0140144 0.0081914 0.0870001 0.0871040 0.136333 0.2052720 NA
22 51219387 rs9616832 T C -0.0147192 0.0106778 0.1700000 0.1680532 0.071803 0.0654952 NA
22 51219704 rs147475742 G A -0.0204888 0.0142050 0.1499999 0.1491993 0.041187 0.0473243 NA
22 51221190 rs369304721 G A -0.0099470 0.0143052 0.4899999 0.4868426 0.048374 NA NA
22 51221731 rs115055839 T C -0.0145973 0.0106801 0.1700000 0.1716957 0.071354 0.0625000 NA
22 51222100 rs114553188 G T -0.0105863 0.0123752 0.3900004 0.3923050 0.054862 0.0880591 NA
22 51223637 rs375798137 G A -0.0112893 0.0124398 0.3599996 0.3641347 0.054481 0.0788738 NA
22 51229805 rs9616985 T C -0.0150513 0.0107131 0.1600000 0.1600381 0.071258 0.0730831 NA
22 51232488 rs376461333 A G -0.0046096 0.0246787 0.8499999 0.8518299 0.020456 NA NA
22 51237063 rs3896457 T C 0.0089949 0.0064674 0.1600000 0.1642829 0.298407 0.2050720 NA

bcf preview

1   49298   rs10399793  T   C   .   PASS    AF=0.623798 ES:SE:LP:AF:ID  -0.00201569:0.00973933:0.0757207:0.623798:rs10399793
1   54676   rs2462492   C   T   .   PASS    AF=0.39916  ES:SE:LP:AF:ID  -0.00947632:0.0097108:0.481486:0.39916:rs2462492
1   86028   rs114608975 T   C   .   PASS    AF=0.103537 ES:SE:LP:AF:ID  -0.00693883:0.0154588:0.187087:0.103537:rs114608975
1   91536   rs6702460   G   T   .   PASS    AF=0.455943 ES:SE:LP:AF:ID  -0.0027564:0.00955226:0.113509:0.455943:rs6702460
1   234313  rs8179466   C   T   .   PASS    AF=0.074448 ES:SE:LP:AF:ID  -0.0103679:0.0188929:0.236572:0.074448:rs8179466
1   534192  rs6680723   C   T   .   PASS    AF=0.242084 ES:SE:LP:AF:ID  0.00295432:0.0108789:0.102373:0.242084:rs6680723
1   546697  rs12025928  A   G   .   PASS    AF=0.912869 ES:SE:LP:AF:ID  0.000253089:0.0135002:0.00436481:0.912869:rs12025928
1   693731  rs12238997  A   G   .   PASS    AF=0.117299 ES:SE:LP:AF:ID  -0.0124141:0.0090724:0.769551:0.117299:rs12238997
1   705882  rs72631875  G   A   .   PASS    AF=0.067692 ES:SE:LP:AF:ID  0.0301009:0.0132241:1.63827:0.067692:rs72631875
1   706368  rs12029736  A   G   .   PASS    AF=0.513316 ES:SE:LP:AF:ID  0.00636967:0.00673373:0.468521:0.513316:rs12029736
1   714596  rs149887893 T   C   .   PASS    AF=0.033688 ES:SE:LP:AF:ID  -0.0272881:0.0167817:1:0.033688:rs149887893
1   715265  rs12184267  C   T   .   PASS    AF=0.03747  ES:SE:LP:AF:ID  -0.0218433:0.0152199:0.823909:0.03747:rs12184267
1   715367  rs12184277  A   G   .   PASS    AF=0.037656 ES:SE:LP:AF:ID  -0.0227699:0.0151404:0.886057:0.037656:rs12184277
1   717485  rs12184279  C   A   .   PASS    AF=0.037233 ES:SE:LP:AF:ID  -0.0210553:0.0152785:0.769551:0.037233:rs12184279
1   717587  rs144155419 G   A   .   PASS    AF=0.016291 ES:SE:LP:AF:ID  0.0288982:0.023961:0.638272:0.016291:rs144155419
1   720381  rs116801199 G   T   .   PASS    AF=0.037873 ES:SE:LP:AF:ID  -0.0211922:0.0150886:0.79588:0.037873:rs116801199
1   721290  rs12565286  G   C   .   PASS    AF=0.037967 ES:SE:LP:AF:ID  -0.0225334:0.0150408:0.886057:0.037967:rs12565286
1   722670  rs116030099 T   C   .   PASS    AF=0.102744 ES:SE:LP:AF:ID  0.0117465:0.0109883:0.537602:0.102744:rs116030099
1   723891  rs2977670   G   C   .   PASS    AF=0.958073 ES:SE:LP:AF:ID  0.0209698:0.0145266:0.823909:0.958073:rs2977670
1   724849  rs12126395  C   A   .   PASS    AF=0.031688 ES:SE:LP:AF:ID  -0.0191072:0.0266067:0.327902:0.031688:rs12126395
1   725060  rs865924913 A   T   .   PASS    AF=0.052721 ES:SE:LP:AF:ID  0.0112223:0.0214321:0.221849:0.052721:rs865924913
1   726794  rs28454925  C   G   .   PASS    AF=0.037461 ES:SE:LP:AF:ID  -0.0217549:0.0151396:0.823909:0.037461:rs28454925
1   729632  rs116720794 C   T   .   PASS    AF=0.03773  ES:SE:LP:AF:ID  -0.020617:0.0150141:0.769551:0.03773:rs116720794
1   729679  rs4951859   C   G   .   PASS    AF=0.841445 ES:SE:LP:AF:ID  0.0171102:0.00784516:1.5376:0.841445:rs4951859
1   730087  rs148120343 T   C   .   PASS    AF=0.056314 ES:SE:LP:AF:ID  -0.0331463:0.0127465:2.03152:0.056314:rs148120343
1   731718  rs58276399  T   C   .   PASS    AF=0.123061 ES:SE:LP:AF:ID  -0.0137446:0.0086177:0.958607:0.123061:rs58276399
1   732989  rs369030935 C   T   .   PASS    AF=0.02512  ES:SE:LP:AF:ID  0.0169464:0.0214639:0.366532:0.02512:rs369030935
1   734349  rs141242758 T   C   .   PASS    AF=0.122315 ES:SE:LP:AF:ID  -0.0135728:0.00862042:0.920819:0.122315:rs141242758
1   736289  rs79010578  T   A   .   PASS    AF=0.134123 ES:SE:LP:AF:ID  -0.0242042:0.00846232:2.37675:0.134123:rs79010578
1   736689  rs181876450 T   C   .   PASS    AF=0.011555 ES:SE:LP:AF:ID  0.0158849:0.0301639:0.221849:0.011555:rs181876450
1   740284  rs61770167  C   T   .   PASS    AF=0.006097 ES:SE:LP:AF:ID  -0.0136391:0.0383056:0.142668:0.006097:rs61770167
1   752478  rs146277091 G   A   .   PASS    AF=0.037613 ES:SE:LP:AF:ID  -0.0203841:0.0148744:0.769551:0.037613:rs146277091
1   752566  rs3094315   G   A   .   PASS    AF=0.837034 ES:SE:LP:AF:ID  0.0164181:0.00758926:1.50864:0.837034:rs3094315
1   752721  rs3131972   A   G   .   PASS    AF=0.836732 ES:SE:LP:AF:ID  0.0168416:0.0075836:1.58503:0.836732:rs3131972
1   753405  rs3115860   C   A   .   PASS    AF=0.868577 ES:SE:LP:AF:ID  0.0125287:0.00814972:0.920819:0.868577:rs3115860
1   753541  rs2073813   G   A   .   PASS    AF=0.130996 ES:SE:LP:AF:ID  -0.01304:0.00817054:0.958607:0.130996:rs2073813
1   754063  rs12184312  G   T   .   PASS    AF=0.038058 ES:SE:LP:AF:ID  -0.0192681:0.0146414:0.721246:0.038058:rs12184312
1   754105  rs12184325  C   T   .   PASS    AF=0.038305 ES:SE:LP:AF:ID  -0.0197015:0.0145506:0.744727:0.038305:rs12184325
1   754182  rs3131969   A   G   .   PASS    AF=0.867984 ES:SE:LP:AF:ID  0.0126685:0.00813652:0.920819:0.867984:rs3131969
1   754192  rs3131968   A   G   .   PASS    AF=0.868058 ES:SE:LP:AF:ID  0.0129262:0.00813985:0.958607:0.868058:rs3131968
1   754211  rs12184313  G   A   .   PASS    AF=0.038212 ES:SE:LP:AF:ID  -0.0206784:0.0146178:0.79588:0.038212:rs12184313
1   754334  rs3131967   T   C   .   PASS    AF=0.867995 ES:SE:LP:AF:ID  0.0125551:0.00813632:0.920819:0.867995:rs3131967
1   754433  rs150578204 G   A   .   PASS    AF=0.005394 ES:SE:LP:AF:ID  -0.0354988:0.0407859:0.420216:0.005394:rs150578204
1   754503  rs3115859   G   A   .   PASS    AF=0.836159 ES:SE:LP:AF:ID  0.0165412:0.00756146:1.5376:0.836159:rs3115859
1   754629  rs10454459  A   G   .   PASS    AF=0.038216 ES:SE:LP:AF:ID  -0.0208801:0.0146391:0.823909:0.038216:rs10454459
1   754964  rs3131966   C   T   .   PASS    AF=0.836793 ES:SE:LP:AF:ID  0.0164579:0.00758221:1.52288:0.836793:rs3131966
1   755240  rs181660517 T   G   .   PASS    AF=0.01303  ES:SE:LP:AF:ID  -0.0260538:0.0274311:0.468521:0.01303:rs181660517
1   755435  rs184270342 T   G   .   PASS    AF=0.005659 ES:SE:LP:AF:ID  0.0390081:0.0407325:0.468521:0.005659:rs184270342
1   755775  rs3131965   A   G   .   PASS    AF=0.838119 ES:SE:LP:AF:ID  0.0149812:0.00768914:1.29243:0.838119:rs3131965
1   755890  rs3115858   A   T   .   PASS    AF=0.868242 ES:SE:LP:AF:ID  0.0119373:0.00812592:0.853872:0.868242:rs3115858